{"id":55658,"date":"2026-11-13T08:59:53","date_gmt":"2026-11-13T16:59:53","guid":{"rendered":"https:\/\/www.griddb.net\/?p=55658"},"modified":"2026-11-13T08:59:53","modified_gmt":"2026-11-13T16:59:53","slug":"electricity-consumption-monitoring-chatbot-with-griddb-and-anthropic-claude","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/electricity-consumption-monitoring-chatbot-with-griddb-and-anthropic-claude\/","title":{"rendered":"Electricity Consumption Monitoring Chatbot with GridDB and Anthropic Claude"},"content":{"rendered":"<p>\nMonitoring household electricity consumption is essential for identifying wasteful patterns and reducing energy costs. However, manually analyzing thousands of hourly readings across multiple appliances is impractical. Combining AI-powered analysis with a high-performance database like <a href=\"https:\/\/griddb.net\/en\/blog\/griddb-cloud-quick-start-guide\/\">GridDB<\/a> allows you to store, retrieve, and analyze electricity data using natural language queries.\n<\/p>\n<p>\nIn this article, you will see how to build an AI-powered electricity consumption chatbot using the <a href=\"https:\/\/www.anthropic.com\/\">Anthropic<\/a> Claude API and GridDB Cloud. The chatbot analyzes 10,000 hourly appliance-level readings, identifies consumption trends, pinpoints peak and off-peak hours for each appliance, and provides data-backed recommendations to save electricity. We will also use AI to create data visualizations showing electricity consumption trends.\n<\/p>\n<p><a href=\"#jupyter\">Note: We have the full jupyter notebook output of this blog below article text.<\/a><\/p>\n<p>\nGridDB\u2019s efficient handling of time-series data, combined with Claude\u2019s analytical reasoning, provides a powerful way to turn raw electricity readings into actionable insights.\n<\/p>\n<p><strong>Prerequisites<\/strong>:<\/p>\n<p>\nYou will need the following to run scripts in this article:\n<\/p>\n<ul>\n<li>A <a href=\"https:\/\/www.global.toshiba\/ww\/products-solutions\/ai-iot\/griddb\/product\/griddb-cloud.html\">GridDB cloud account<\/a>. Refer to this <a href=\"https:\/\/griddb.net\/en\/blog\/griddb-cloud-quick-start-guide\/\">quick start guide<\/a> to set up a GridDB cloud account.<\/li>\n<li><a href=\"https:\/\/platform.claude.com\/\">Anthropic API Key<\/a>. You can obtain one from the Anthropic console.<\/li>\n<\/ul>\n<p>\nNote: You can find the complete code for this tutorial in my <a href=\"https:\/\/github.com\/usmanmalik57\/GridDB-Blogs\/tree\/main\/Electricity%20Consumption%20Monitoring%20Chatbot%20with%20GridDB%20and%20Anthropic%20Claude\">GridDB Blogs GitHub repository<\/a>.\n<\/p>\n<h2 id=\"installing-and-importing-required-libraries\">Installing and Importing Required Libraries<\/h2>\n<p>\nThe following installs the libraries you will need to run the scripts in this article.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-plaintext\">!pip install python-dotenv\r\n!pip install anthropic<\/code><\/pre>\n<\/div>\n<p>\nThe script below imports the required libraries.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">import pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport base64\r\nimport os\r\nimport requests\r\nimport json\r\nimport re\r\n\r\nfrom dotenv import load_dotenv\r\nload_dotenv()<\/code><\/pre>\n<\/div>\n<h2 id=\"importing-the-dataset\">Importing the Dataset<\/h2>\n<p>\nThe dataset used in this article provides hourly electricity consumption data recorded at the appliance level. It includes readings for six appliances: air conditioner (ac), fridge, lights, fans, washing machine, and TV. Each row represents one hourly reading for a household, along with metadata such as the season and any ongoing festival. You can <a href=\"https:\/\/www.kaggle.com\/datasets\/frank451995\/appliance-wise-hourly-electricity-consumption?resource=download\">download the dataset from Kaggle<\/a>.\n<\/p>\n<p>\nThe following script loads the dataset and selects the first 10,000 records. The original dataset contains more than 400k records; however, 10k records are sufficient for this sample analysis.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset = pd.read_csv(&quot;appliance_usage_dataset.csv&quot;,\r\n                      encoding = &#x27;utf-8&#x27;)\r\ndataset = dataset[:10000]\r\nprint(dataset.shape)\r\ndataset.head()<\/code><\/pre>\n<\/div>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"\/wp-content\/uploads\/2026\/11\/img1-dataset.png\"><img fetchpriority=\"high\" decoding=\"async\" src=\"\/wp-content\/uploads\/2026\/11\/img1-dataset.png\" alt=\"\" width=\"689\" height=\"208\" class=\"aligncenter size-full wp-image-55661\" srcset=\"\/wp-content\/uploads\/2026\/11\/img1-dataset.png 689w, \/wp-content\/uploads\/2026\/11\/img1-dataset-300x91.png 300w, \/wp-content\/uploads\/2026\/11\/img1-dataset-600x181.png 600w\" sizes=\"(max-width: 689px) 100vw, 689px\" \/><\/a><\/p>\n<h2 id=\"inserting-data-in-griddb\">Inserting Data in GridDB<\/h2>\n<p>\nWe will insert the data into GridDB Cloud.\n<\/p>\n<h3 id=\"creating-a-griddb-connection\">Creating a GridDB Connection<\/h3>\n<p>\nRun the following script to test your connection with the GridDB cloud. You can retrieve the connection credentials from your GridDB cloud account.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">username = os.environ.get(&quot;username&quot;)\r\npassword = os.environ.get(&quot;password&quot;)\r\nbase_url = os.environ.get(&quot;base_url&quot;)\r\n\r\n\r\nurl = f&quot;{base_url}\/checkConnection&quot;\r\n\r\ncredentials = f&quot;{username}:{password}&quot;\r\nencoded_credentials = base64.b64encode(credentials.encode()).decode()\r\n\r\nheaders = {\r\n    &#x27;Content-Type&#x27;: &#x27;application\/json&#x27;,  # Added this header to specify JSON content\r\n    &#x27;Authorization&#x27;: f&#x27;Basic {encoded_credentials}&#x27;,\r\n    &#x27;User-Agent&#x27;: &#x27;PostmanRuntime\/7.29.0&#x27;\r\n}\r\n\r\nresponse = requests.get(url, headers=headers)\r\n\r\nprint(response.status_code)<\/code><\/pre>\n<\/div>\n<p><strong>Output:<\/strong><\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-plaintext\">200<\/code><\/pre>\n<\/div>\n<p>\nIf you do not see the above response, check this <a href=\"https:\/\/griddb.net\/en\/blog\/griddb-cloud-quick-start-guide\/\">quick start guide<\/a> for troubleshooting.\n<\/p>\n<h3 id=\"creating-a-griddb-container-for-electricity-consumption-data\">Creating a GridDB Container for Electricity Consumption Data<\/h3>\n<p>\nBefore inserting data, we need to create a container in GridDB with the correct schema. The following script maps pandas data types to GridDB types.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset.insert(0, &quot;SerialNo&quot;, dataset.index + 1)\r\ndataset.columns.name = None   \r\n# Mapping pandas dtypes to GridDB types\r\ntype_mapping = {\r\n    &quot;int64&quot;:          &quot;LONG&quot;,\r\n    &quot;float64&quot;:        &quot;DOUBLE&quot;,\r\n    &quot;bool&quot;:           &quot;BOOL&quot;,\r\n    &#x27;datetime64&#x27;: &quot;TIMESTAMP&quot;,\r\n    &quot;object&quot;:         &quot;STRING&quot;,\r\n    &quot;category&quot;:       &quot;STRING&quot;,\r\n}\r\n\r\n# Generate the columns part of the payload dynamically\r\ncolumns = []\r\nfor col, dtype in dataset.dtypes.items():\r\n    griddb_type = type_mapping.get(str(dtype), &quot;STRING&quot;)  # Default to STRING if unknown\r\n    columns.append({\r\n        &quot;name&quot;: col,\r\n        &quot;type&quot;: griddb_type\r\n    })\r\n\r\nprint(columns)<\/code><\/pre>\n<\/div>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"\/wp-content\/uploads\/2026\/11\/img2-pandas-griddb-mapping-types.png\"><img decoding=\"async\" src=\"\/wp-content\/uploads\/2026\/11\/img2-pandas-griddb-mapping-types.png\" alt=\"\" width=\"1099\" height=\"70\" class=\"aligncenter size-full wp-image-55662\" srcset=\"\/wp-content\/uploads\/2026\/11\/img2-pandas-griddb-mapping-types.png 1099w, \/wp-content\/uploads\/2026\/11\/img2-pandas-griddb-mapping-types-300x19.png 300w, \/wp-content\/uploads\/2026\/11\/img2-pandas-griddb-mapping-types-1024x65.png 1024w, \/wp-content\/uploads\/2026\/11\/img2-pandas-griddb-mapping-types-768x49.png 768w, \/wp-content\/uploads\/2026\/11\/img2-pandas-griddb-mapping-types-600x38.png 600w\" sizes=\"(max-width: 1099px) 100vw, 1099px\" \/><\/a><\/p>\n<p>\nNext, we create the container using the GridDB REST API.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">container_name = &quot;electricity_consumption_db&quot;\r\n\r\nurl = f&quot;{base_url}\/containers&quot;\r\n\r\n# Create the payload for the POST request\r\npayload = json.dumps({\r\n    &quot;container_name&quot;: container_name,\r\n    &quot;container_type&quot;: &quot;COLLECTION&quot;,\r\n    &quot;rowkey&quot;: True,  # Assuming the first column as rowkey\r\n    &quot;columns&quot;: columns\r\n})\r\n\r\n\r\n# Make the POST request to create the container\r\nresponse = requests.post(url, headers=headers, data=payload)\r\n\r\n# Print the response\r\nprint(f&quot;Status Code: {response.status_code}&quot;)<\/code><\/pre>\n<\/div>\n<p><strong>Output:<\/strong><\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-plaintext\">Status Code: 201<\/code><\/pre>\n<\/div>\n<p>\nThe 201 status code confirms the container has been created successfully.\n<\/p>\n<h3 id=\"inserting-data-in-electricity-consumption-griddb-container\">Inserting Data in Electricity Consumption GridDB Container<\/h3>\n<p>\nWith the container created, we can now insert the 10,000 rows of electricity consumption data. The <code>format_row<\/code> function handles type conversions, including converting NaN values to None for GridDB compatibility.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">url = f&quot;{base_url}\/containers\/{container_name}\/rows&quot;\r\n# Convert dataset to list of lists (row-wise) with proper formatting\r\n\r\ndef format_row(row):\r\n    formatted = []\r\n    for item in row:\r\n        if pd.isna(item):\r\n            formatted.append(None)  # Convert NaN to None\r\n        elif isinstance(item, bool):\r\n            formatted.append(str(item).lower())  # Convert True\/False to true\/false\r\n        elif isinstance(item, (int, float)):\r\n            formatted.append(item)  # Keep integers and floats as they are\r\n        else:\r\n            formatted.append(str(item))  # Convert other types to string\r\n    return formatted\r\n\r\n# Prepare rows with correct formatting\r\nrows = [format_row(row) for row in dataset.values.tolist()]\r\n\r\n# Create payload as a JSON string\r\npayload = json.dumps(rows)\r\n\r\n# Make the PUT request to add the rows to the container\r\nresponse = requests.put(url, headers=headers, data=payload)\r\n\r\n# Print the response\r\nprint(f&quot;Status Code: {response.status_code}&quot;)\r\nprint(f&quot;Response Text: {response.text}&quot;)<\/code><\/pre>\n<\/div>\n<p><strong>Output:<\/strong><\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-plaintext\">Status Code: 200\r\nResponse Text: {&quot;count&quot;:10000}<\/code><\/pre>\n<\/div>\n<p>\nAt this point, all 10,000 records have been successfully inserted into the GridDB container.\n<\/p>\n<h2 id=\"using-ai-to-monitor-electricity-consumption-and-get-recommendations\">Using AI to Monitor Electricity Consumption and Get Recommendations<\/h2>\n<p>\nNow that the data is stored in GridDB, we can retrieve it and use the Anthropic Claude API to analyze consumption patterns and provide recommendations. The approach involves three steps: retrieve data from GridDB, build a pre-computed summary of the dataset, and pass that summary to Claude as context for intelligent Q&#038;A.\n<\/p>\n<h3 id=\"retrieve-data-from-griddb\">Retrieve Data from GridDB<\/h3>\n<p>\nThe following script retrieves all 10,000 records from the GridDB container and loads them into a pandas DataFrame.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">\r\nurl = f&quot;{base_url}\/containers\/{container_name}\/rows&quot;\r\n\r\n# Define the payload for the query\r\npayload = json.dumps({\r\n    &quot;offset&quot;: 0,           # Start from the first row\r\n    &quot;limit&quot;: 10000,         # Limit the number of rows returned\r\n    &quot;condition&quot;: &quot;&quot;,       # No filtering condition (you can customize it)\r\n    &quot;sort&quot;: &quot;&quot;             # No sorting (you can customize it)\r\n})\r\n\r\n# Make the POST request to read data from the container\r\nresponse = requests.post(url, headers=headers, data=payload)\r\n\r\n# Check response status and print output\r\nprint(f&quot;Status Code: {response.status_code}&quot;)\r\nif response.status_code == 200:\r\n    try:\r\n        data = response.json()\r\n        print(&quot;Data retrieved successfully!&quot;)\r\n\r\n        # Convert the response to a DataFrame\r\n        rows = data.get(&quot;rows&quot;, [])\r\n        electricity_consumption_db = pd.DataFrame(rows, columns=[col for col in dataset.columns])\r\n\r\n    except json.JSONDecodeError:\r\n        print(&quot;Error: Failed to decode JSON response.&quot;)\r\nelse:\r\n    print(f&quot;Error: Failed to query data from the container. Response: {response.text}&quot;)\r\n\r\nprint(electricity_consumption_db.shape)\r\nelectricity_consumption_db.head()<\/code><\/pre>\n<\/div>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"\/wp-content\/uploads\/2026\/11\/img3-griddb-retrieved-data.png\"><img decoding=\"async\" src=\"\/wp-content\/uploads\/2026\/11\/img3-griddb-retrieved-data.png\" alt=\"\" width=\"770\" height=\"221\" class=\"aligncenter size-full wp-image-55663\" srcset=\"\/wp-content\/uploads\/2026\/11\/img3-griddb-retrieved-data.png 770w, \/wp-content\/uploads\/2026\/11\/img3-griddb-retrieved-data-300x86.png 300w, \/wp-content\/uploads\/2026\/11\/img3-griddb-retrieved-data-768x220.png 768w, \/wp-content\/uploads\/2026\/11\/img3-griddb-retrieved-data-600x172.png 600w, \/wp-content\/uploads\/2026\/11\/img3-griddb-retrieved-data-350x100.png 350w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/a><\/p>\n<p>\nThe data has been successfully retrieved from GridDB and is ready for analysis.\n<\/p>\n<h3 id=\"build-dataset-summary\">Build Dataset Summary<\/h3>\n<p>\nSince we cannot send 10,000 raw rows to Claude in a single prompt because it may exceed token limits and reduce response quality, we pre-compute comprehensive aggregations and pass those as context instead. This way, Claude reasons over actual statistics computed from all 10,000 records.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">import pandas as pd\r\n\r\nAPPLIANCE_COLS = [&#x27;ac&#x27;, &#x27;fridge&#x27;, &#x27;lights&#x27;, &#x27;fans&#x27;, &#x27;washing_machine&#x27;, &#x27;tv&#x27;]\r\n\r\n# Prepare the dataframe\r\nelectricity_consumption_db[&#x27;timestamp&#x27;] = pd.to_datetime(electricity_consumption_db[&#x27;timestamp&#x27;])\r\nelectricity_consumption_db[&#x27;Hour&#x27;] = electricity_consumption_db[&#x27;timestamp&#x27;].dt.hour\r\nelectricity_consumption_db[&#x27;DayOfWeek&#x27;] = electricity_consumption_db[&#x27;timestamp&#x27;].dt.day_name()\r\nelectricity_consumption_db[&#x27;Month&#x27;] = electricity_consumption_db[&#x27;timestamp&#x27;].dt.month\r\nelectricity_consumption_db[&#x27;total_kwh&#x27;] = electricity_consumption_db[APPLIANCE_COLS].sum(axis=1)\r\n\r\n\r\ndef build_summary(df):\r\n    parts = []\r\n\r\n    # Overview\r\n    parts.append(f&quot;Records: {len(df)} hourly readings across {df[&#x27;house_id&#x27;].nunique()} households&quot;)\r\n    parts.append(f&quot;Period: {df[&#x27;timestamp&#x27;].min()} to {df[&#x27;timestamp&#x27;].max()}&quot;)\r\n    parts.append(f&quot;Seasons: {df[&#x27;season&#x27;].unique().tolist()}&quot;)\r\n    parts.append(f&quot;Festivals: {df[&#x27;festival&#x27;].unique().tolist()}&quot;)\r\n\r\n    # Appliance stats\r\n    parts.append(f&quot;\\n--- Appliance Stats (kWh) ---\\n{df[APPLIANCE_COLS].describe().round(3).to_string()}&quot;)\r\n\r\n    # Total energy ranking\r\n    totals = df[APPLIANCE_COLS].sum().sort_values(ascending=False)\r\n    pct = (totals \/ totals.sum() * 100).round(1)\r\n    parts.append(f&quot;\\n--- Energy Ranking ---&quot;)\r\n    for app in totals.index:\r\n        parts.append(f&quot;  {app}: {totals[app]:.1f} kWh ({pct[app]}%)&quot;)\r\n\r\n    # Hourly patterns\r\n    hourly = df.groupby(&#x27;Hour&#x27;)[APPLIANCE_COLS].mean().round(4)\r\n    hourly_total = df.groupby(&#x27;Hour&#x27;)[&#x27;total_kwh&#x27;].mean().round(2)\r\n    parts.append(f&quot;\\n--- Hourly Avg Consumption (kWh) ---\\n{hourly.to_string()}&quot;)\r\n    parts.append(f&quot;\\nHourly total avg:\\n{hourly_total.to_string()}&quot;)\r\n    parts.append(f&quot;Lowest hour: {hourly_total.idxmin()}:00 ({hourly_total.min()} kWh)&quot;)\r\n    parts.append(f&quot;Highest hour: {hourly_total.idxmax()}:00 ({hourly_total.max()} kWh)&quot;)\r\n\r\n    # Peak per appliance\r\n    parts.append(f&quot;\\n--- Peak\/Low Hour per Appliance ---&quot;)\r\n    for col in APPLIANCE_COLS:\r\n        parts.append(f&quot;  {col}: peak {hourly[col].idxmax()}:00 ({hourly[col].max()}), low {hourly[col].idxmin()}:00 ({hourly[col].min()})&quot;)\r\n\r\n    # Season patterns\r\n    seasonal = df.groupby(&#x27;season&#x27;)[APPLIANCE_COLS].mean().round(4)\r\n    parts.append(f&quot;\\n--- Seasonal Avg (kWh) ---\\n{seasonal.to_string()}&quot;)\r\n\r\n    # Day of week\r\n    day_order = [&#x27;Monday&#x27;, &#x27;Tuesday&#x27;, &#x27;Wednesday&#x27;, &#x27;Thursday&#x27;, &#x27;Friday&#x27;, &#x27;Saturday&#x27;, &#x27;Sunday&#x27;]\r\n    daily = df.groupby(&#x27;DayOfWeek&#x27;)[&#x27;total_kwh&#x27;].mean().reindex(day_order).round(2)\r\n    parts.append(f&quot;\\n--- Day of Week Avg Total (kWh) ---\\n{daily.to_string()}&quot;)\r\n\r\n    # Household comparison (top 5 and bottom 5)\r\n    hh = df.groupby(&#x27;house_id&#x27;)[&#x27;total_kwh&#x27;].mean().sort_values(ascending=False).round(2)\r\n    parts.append(f&quot;\\n--- Household Avg Hourly (kWh) ---&quot;)\r\n    parts.append(f&quot;Top 5:\\n{hh.head().to_string()}&quot;)\r\n    parts.append(f&quot;Bottom 5:\\n{hh.tail().to_string()}&quot;)\r\n\r\n    # Festival impact\r\n    fest = df.groupby(&#x27;festival&#x27;)[&#x27;total_kwh&#x27;].mean().round(2)\r\n    parts.append(f&quot;\\n--- Festival Impact (avg kWh) ---\\n{fest.to_string()}&quot;)\r\n\r\n    return &quot;\\n&quot;.join(parts)\r\n\r\n\r\ndata_summary = build_summary(electricity_consumption_db)<\/code><\/pre>\n<\/div>\n<p>\nThe <code>build_summary<\/code> function in the above script computes seven categories of aggregations: overall statistics, appliance energy rankings with percentage shares, hourly consumption patterns with peak and off-peak hours for each appliance, seasonal averages, day-of-week trends, household comparisons, and festival impact analysis. These aggregations cover all 10,000 records and give Claude a comprehensive view of the data.\n<\/p>\n<h3 id=\"create-chatbot\">Create Chatbot<\/h3>\n<p>\nWith the data summary ready, we create the chatbot. The system prompt embeds the entire summary and instructs Claude to always cite specific numbers from the data. The <code>ElectricityChatbot<\/code> class maintains conversation history, allowing follow-up questions that build on previous answers.\n<\/p>\n<p>\nIn the script below, We used the Claude 4.6 sonnet model for reasoning. You can use any other anthropic model if you want.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">SYSTEM_PROMPT = f&quot;&quot;&quot;You are an electricity consumption analyst. You have data from {electricity_consumption_db[&#x27;house_id&#x27;].nunique()} households\r\nwith {len(electricity_consumption_db)} hourly readings. Appliance columns: ac, fridge, lights, fans, washing_machine, tv.\r\nAll values are in kWh. The data also has season and festival columns.\r\n\r\nAlways cite specific numbers. Estimate kWh savings when giving recommendations.\r\n\r\nDATA:\r\n{data_summary}\r\n&quot;&quot;&quot;\r\n\r\n\r\nclass ElectricityChatbot:\r\n    def __init__(self):\r\n        self.client = anthropic.Anthropic()\r\n        self.history = []\r\n\r\n    def ask(self, question):\r\n        self.history.append({&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: question})\r\n        response = self.client.messages.create(\r\n            model=&quot;claude-sonnet-4-6&quot;,\r\n            max_tokens=2048,\r\n            system=SYSTEM_PROMPT,\r\n            messages=self.history\r\n        )\r\n        answer = response.content[0].text\r\n        self.history.append({&quot;role&quot;: &quot;assistant&quot;, &quot;content&quot;: answer})\r\n        return answer\r\n\r\n    def reset(self):\r\n        self.history = []\r\n        print(&quot;History cleared.&quot;)\r\n\r\n\r\nchatbot = ElectricityChatbot()\r\nprint(&quot;Chatbot ready!&quot;)<\/code><\/pre>\n<\/div>\n<p><strong>Output:<\/strong><\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-plaintext\">Chatbot ready!<\/code><\/pre>\n<\/div>\n<p>\nThe chatbot is initialized and ready to answer questions about the electricity data.\n<\/p>\n<h3 id=\"ask-questions\">Ask Questions<\/h3>\n<p>\nLet&#8217;s test the chatbot with questions about electricity consumption trends, savings recommendations, and targeted advice.\n<\/p>\n<h4 id=\"question-1-electricity-consumption-trends\">Question 1: Electricity Consumption Trends<\/h4>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">print(chatbot.ask(&quot;What are the main electricity consumption trends? Break down by appliance and season.&quot;))<\/code><\/pre>\n<\/div>\n<p><strong>Output (partial screenshot):<\/strong><br \/>\n<a href=\"\/wp-content\/uploads\/2026\/11\/img4-electricity-trends-partial-output.png\"><img loading=\"lazy\" decoding=\"async\" src=\"\/wp-content\/uploads\/2026\/11\/img4-electricity-trends-partial-output.png\" alt=\"\" width=\"1076\" height=\"303\" class=\"aligncenter size-full wp-image-55664\" srcset=\"\/wp-content\/uploads\/2026\/11\/img4-electricity-trends-partial-output.png 1076w, \/wp-content\/uploads\/2026\/11\/img4-electricity-trends-partial-output-300x84.png 300w, \/wp-content\/uploads\/2026\/11\/img4-electricity-trends-partial-output-1024x288.png 1024w, \/wp-content\/uploads\/2026\/11\/img4-electricity-trends-partial-output-768x216.png 768w, \/wp-content\/uploads\/2026\/11\/img4-electricity-trends-partial-output-600x169.png 600w, \/wp-content\/uploads\/2026\/11\/img4-electricity-trends-partial-output-350x100.png 350w\" sizes=\"(max-width: 1076px) 100vw, 1076px\" \/><\/a><\/p>\n<p>\nThe chatbot provides a detailed breakdown of consumption patterns. It identifies the air conditioner as the highest single consumer at 4,689 kWh (18.8% of total), followed closely by fans at 4,492 kWh (18.1%) and fridge at 4,491 kWh (18.0%). The analysis reveals striking seasonal patterns: AC consumption jumps to 1.058 kWh\/hr in summer (a 3.5x increase over winter), while fans and washing machines surge dramatically during the rainy season. The fridge and TV remain remarkably stable across all seasons, making them low-priority targets for optimization.\n<\/p>\n<h4 id=\"question-2-savings-recommendations\">Question 2: Savings Recommendations<\/h4>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">print(chatbot.ask(&quot;Give me specific recommendations to save electricity with estimated kWh savings for each.&quot;))<\/code><\/pre>\n<\/div>\n<p><strong>Output (partial screenshot):<\/strong><\/p>\n<p><a href=\"\/wp-content\/uploads\/2026\/11\/img5-savings-recommendations-partial-output.png\"><img loading=\"lazy\" decoding=\"async\" src=\"\/wp-content\/uploads\/2026\/11\/img5-savings-recommendations-partial-output.png\" alt=\"\" width=\"1076\" height=\"303\" class=\"aligncenter size-full wp-image-55665\" srcset=\"\/wp-content\/uploads\/2026\/11\/img5-savings-recommendations-partial-output.png 1076w, \/wp-content\/uploads\/2026\/11\/img5-savings-recommendations-partial-output-300x84.png 300w, \/wp-content\/uploads\/2026\/11\/img5-savings-recommendations-partial-output-1024x288.png 1024w, \/wp-content\/uploads\/2026\/11\/img5-savings-recommendations-partial-output-768x216.png 768w, \/wp-content\/uploads\/2026\/11\/img5-savings-recommendations-partial-output-600x169.png 600w, \/wp-content\/uploads\/2026\/11\/img5-savings-recommendations-partial-output-350x100.png 350w\" sizes=\"(max-width: 1076px) 100vw, 1076px\" \/><\/a><\/p>\n<p>\nClaude provides prioritized recommendations with estimated kWh savings based on the actual data. The top recommendations include: raising the AC thermostat by 2\u00b0C in summer (saving approximately 278 kWh\/year), setting AC auto-shutoff during winter nights (saving approximately 383 kWh\/year), replacing non-LED lights (saving approximately 670 kWh\/year), and addressing the unusual 5 AM lighting peak which suggests lights being left on overnight (saving approximately 420 kWh\/year). Each recommendation includes the specific data points that support it.\n<\/p>\n<h4 id=\"question-3-best-single-change\">Question 3: Best Single Change<\/h4>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">print(chatbot.ask(&quot;Which single change gives the biggest savings with least effort?&quot;))<\/code><\/pre>\n<\/div>\n<p><strong>Output (partial screenshot):<\/strong><\/p>\n<p><a href=\"\/wp-content\/uploads\/2026\/11\/img6-single-saving-advice-partial-output.png\"><img loading=\"lazy\" decoding=\"async\" src=\"\/wp-content\/uploads\/2026\/11\/img6-single-saving-advice-partial-output.png\" alt=\"\" width=\"818\" height=\"359\" class=\"aligncenter size-full wp-image-55666\" srcset=\"\/wp-content\/uploads\/2026\/11\/img6-single-saving-advice-partial-output.png 818w, \/wp-content\/uploads\/2026\/11\/img6-single-saving-advice-partial-output-300x132.png 300w, \/wp-content\/uploads\/2026\/11\/img6-single-saving-advice-partial-output-768x337.png 768w, \/wp-content\/uploads\/2026\/11\/img6-single-saving-advice-partial-output-600x263.png 600w\" sizes=\"(max-width: 818px) 100vw, 818px\" \/><\/a><\/p>\n<p>\nBecause the chatbot maintains conversation history, this follow-up question builds on the previous analysis. Claude identifies the overnight lighting issue as the single best change: the data shows lights peaking at 5 AM (0.384 kWh\/hr) when they should be near zero, suggesting lights are left on overnight.\n<\/p>\n<h3 id=\"execute-code-and-generate-charts\">Execute Code and Generate Charts<\/h3>\n<p>\nIn addition to natural language analysis, we can ask Claude to generate matplotlib code for visualizations. The <code>generate_and_run_chart<\/code> function sends a chart description to Claude, receives executable Python code, and runs it inline to display the chart.\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">def generate_and_run_chart(question):\r\n    &quot;&quot;&quot;\r\n    Ask Claude to generate matplotlib code for a chart, then execute it.\r\n    The chart displays inline in the notebook.\r\n    &quot;&quot;&quot;\r\n    schema = f&quot;Columns: {list(electricity_consumption_db.columns)}\\nAppliance cols: {APPLIANCE_COLS}\\nSample:\\n{electricity_consumption_db.head(3).to_csv(index=False)}&quot;\r\n\r\n    response = client.messages.create(\r\n        model=&quot;claude-sonnet-4-6&quot;,\r\n        max_tokens=2048,\r\n        messages=[{\r\n            &quot;role&quot;: &quot;user&quot;,\r\n            &quot;content&quot;: f&quot;&quot;&quot;I have a pandas DataFrame `electricity_consumption_db` with this structure:\r\n\r\n{schema}\r\n\r\nThe electricity_consumption_db already has &#x27;Hour&#x27;, &#x27;DayOfWeek&#x27;, &#x27;Month&#x27;, &#x27;total_kwh&#x27; columns.\r\nAppliance values (ac, fridge, lights, fans, washing_machine, tv) are in kWh.\r\n\r\nWrite Python code using pandas and matplotlib to: {question}\r\n\r\nRULES:\r\n- Return ONLY Python code, no markdown fences, no explanation\r\n- df is already loaded, matplotlib.pyplot is imported as plt\r\n- Use plt.figure(figsize=(10, 6)) for good sizing\r\n- Always include plt.tight_layout() and plt.show()\r\n- Add clear title, axis labels, and legend where needed&quot;&quot;&quot;\r\n        }]\r\n    )\r\n\r\n    code = response.content[0].text\r\n    # Strip markdown fences if Claude includes them\r\n    code = re.sub(r&#x27;^<\/code><\/pre>\n<\/div>\n<p>\npython\\s*&#8217;, &#8221;, code, flags=re.MULTILINE)<br \/>\n    code = re.sub(r&#8217;^<code><code>\\s*$', '', code, flags=re.MULTILINE)<br \/>\n    code = code.strip()\n<\/p>\n<p>\n    print(\"Generated code:\")<br \/>\n    print(\"-\" * 40)<br \/>\n    print(code)<br \/>\n    print(\"-\" * 40)<br \/>\n    print(\"\\nExecuting...\\n\")\n<\/p>\n<p>\n    exec(code, globals())\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-plaintext\">\r\nThe `generate_and_run_chart` function sends the dataframe schema and a natural language description to Claude, which returns ready-to-execute matplotlib code. The `exec(code, globals())` call runs the code in the global scope so that all variables (including electricity_consumption_db, plt, and pd) are accessible.\r\nLet&#x27;s plot some charts showing electricity consumption trends.\r\n\r\n#### Chart 1: Hourly Consumption by Appliance<\/code><\/pre>\n<\/div>\n<p>\npython<br \/>\ngenerate_and_run_chart(\"Line chart showing average hourly consumption for each appliance, all on one plot with different colors and a legend.\")\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-plaintext\">\r\n**Output:**\r\n\r\n&lt;img src=&quot;images\\img7-average-hourly-consumption-by-appliance.png&quot;&gt;\r\n\r\n\r\n#### Chart 2: Total Energy by Appliance<\/code><\/pre>\n<\/div>\n<p>\npython<br \/>\ngenerate_and_run_chart(\"Bar chart showing total energy consumption per appliance, sorted highest to lowest, with kWh values on each bar.\")\n<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-plaintext\">\r\n**Output:**\r\n\r\n&lt;img src=&quot;images\\img8-total-energy-consumptio-per-appliance.png&quot;&gt;\r\n\r\nThe bar chart confirms AC as the highest total consumer at 4,689 kWh, followed by fans and fridge, with washing machine consuming the least at 3,487 kWh.\r\n\r\n#### Chart 3: Seasonal Patterns<\/code><\/pre>\n<\/div>\n<p>\npython<br \/>\ngenerate_and_run_chart(\"Bar chart showing daily average consumption of each device for all the seasons\")\n<\/p>\n<p><\/code><\/code><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"\/wp-content\/uploads\/2026\/11\/img9-avareage-daily-consumption-by-season.png\"><img loading=\"lazy\" decoding=\"async\" src=\"\/wp-content\/uploads\/2026\/11\/img9-avareage-daily-consumption-by-season.png\" alt=\"\" width=\"992\" height=\"585\" class=\"aligncenter size-full wp-image-55669\" srcset=\"\/wp-content\/uploads\/2026\/11\/img9-avareage-daily-consumption-by-season.png 992w, \/wp-content\/uploads\/2026\/11\/img9-avareage-daily-consumption-by-season-300x177.png 300w, \/wp-content\/uploads\/2026\/11\/img9-avareage-daily-consumption-by-season-768x453.png 768w, \/wp-content\/uploads\/2026\/11\/img9-avareage-daily-consumption-by-season-600x354.png 600w\" sizes=\"(max-width: 992px) 100vw, 992px\" \/><\/a><\/p>\n<p>\nThe seasonal chart highlights the dramatic AC spike in summer and the fan and washing machine surges during the rainy season, while fridge and TV remain stable across all seasons.\n<\/p>\n<h2 id=\"conclusion\">Conclusion<\/h2>\n<p>\nThis article demonstrates how to use the Anthropic Claude API and GridDB Cloud to build an AI-powered electricity consumption chatbot, which delivers data-backed analysis of consumption trends, identifies peak and off-peak hours for each appliance, and provides actionable savings recommendations with estimated kWh reductions. The code generation capability extends the chatbot further by producing inline matplotlib visualizations on demand, making it easy to explore the data visually without writing chart code manually.\n<\/p>\n<p>\nYou can extend this approach by adding more households for comparative analysis, integrating real-time data feeds from smart meters, or combining the chatbot with GridDB&#8217;s time-series querying capabilities for more granular analysis. The combination of a high-performance database and AI reasoning opens up possibilities for intelligent energy monitoring at scale.\n<\/p>\n<p>\nYou can find the complete code for this blog on the <a href=\"https:\/\/github.com\/usmanmalik57\/GridDB-Blogs\/tree\/main\/Electricity%20Consumption%20Monitoring%20Chatbot%20with%20GridDB%20and%20Anthropic%20Claude\">GridDB Blogs GitHub repository<\/a>. If you have any questions or queries related to GridDB, create a post on Stack Overflow with the <code>griddb<\/code> tag. Our engineers are more than happy to respond.\n<\/p>\n<h3 id=\"jupyter\"> Jupyter Notebook Output <\/h3>\n<div class=\"notebook\">\n    <div class=\"nbconvert\">\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=285e02a5-fca1-485c-a0b3-feba23f48192\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"Installing-and-Importing-Required-Libraries\">Installing and Importing Required Libraries<a class=\"anchor-link\" href=\"#Installing-and-Importing-Required-Libraries\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=d58e0202-7b50-4890-951d-d400581aff18\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[30]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"o\">!<\/span>pip<span class=\"w\"> <\/span>install<span class=\"w\"> <\/span>python-dotenv\n<span class=\"o\">!<\/span>pip<span class=\"w\"> <\/span>install<span class=\"w\"> <\/span>anthropic\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>Defaulting to user installation because normal site-packages is not writeable\nRequirement already satisfied: python-dotenv in \/home\/mani\/.local\/lib\/python3.10\/site-packages (1.2.1)\n\n<span class=\"ansi-bold\">[<\/span><span class=\"ansi-blue-fg\">notice<\/span><span class=\"ansi-bold\">]<\/span> A new release of pip is available: <span class=\"ansi-red-fg\">25.3<\/span> -&gt; <span class=\"ansi-green-fg\">26.0.1<\/span>\n<span class=\"ansi-bold\">[<\/span><span class=\"ansi-blue-fg\">notice<\/span><span class=\"ansi-bold\">]<\/span> To update, run: <span class=\"ansi-green-fg\">python3 -m pip install --upgrade pip<\/span>\nDefaulting to user installation because normal site-packages is not writeable\nCollecting anthropic\n  Downloading anthropic-0.84.0-py3-none-any.whl.metadata (3.0 kB)\nRequirement already satisfied: anyio&lt;5,&gt;=3.5.0 in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from anthropic) (4.3.0)\nRequirement already satisfied: distro&lt;2,&gt;=1.7.0 in \/usr\/lib\/python3\/dist-packages (from anthropic) (1.7.0)\nCollecting docstring-parser&lt;1,&gt;=0.15 (from anthropic)\n  Using cached docstring_parser-0.17.0-py3-none-any.whl.metadata (3.5 kB)\nRequirement already satisfied: httpx&lt;1,&gt;=0.25.0 in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from anthropic) (0.28.1)\nRequirement already satisfied: jiter&lt;1,&gt;=0.4.0 in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from anthropic) (0.7.0)\nRequirement already satisfied: pydantic&lt;3,&gt;=1.9.0 in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from anthropic) (2.11.7)\nRequirement already satisfied: sniffio in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from anthropic) (1.3.1)\nRequirement already satisfied: typing-extensions&lt;5,&gt;=4.10 in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from anthropic) (4.15.0)\nRequirement already satisfied: idna&gt;=2.8 in \/usr\/lib\/python3\/dist-packages (from anyio&lt;5,&gt;=3.5.0-&gt;anthropic) (3.3)\nRequirement already satisfied: exceptiongroup&gt;=1.0.2 in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from anyio&lt;5,&gt;=3.5.0-&gt;anthropic) (1.3.0)\nRequirement already satisfied: certifi in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from httpx&lt;1,&gt;=0.25.0-&gt;anthropic) (2025.10.5)\nRequirement already satisfied: httpcore==1.* in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from httpx&lt;1,&gt;=0.25.0-&gt;anthropic) (1.0.4)\nRequirement already satisfied: h11&lt;0.15,&gt;=0.13 in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from httpcore==1.*-&gt;httpx&lt;1,&gt;=0.25.0-&gt;anthropic) (0.14.0)\nRequirement already satisfied: annotated-types&gt;=0.6.0 in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from pydantic&lt;3,&gt;=1.9.0-&gt;anthropic) (0.7.0)\nRequirement already satisfied: pydantic-core==2.33.2 in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from pydantic&lt;3,&gt;=1.9.0-&gt;anthropic) (2.33.2)\nRequirement already satisfied: typing-inspection&gt;=0.4.0 in \/home\/mani\/.local\/lib\/python3.10\/site-packages (from pydantic&lt;3,&gt;=1.9.0-&gt;anthropic) (0.4.0)\nDownloading anthropic-0.84.0-py3-none-any.whl (455 kB)\nUsing cached docstring_parser-0.17.0-py3-none-any.whl (36 kB)\nInstalling collected packages: docstring-parser, anthropic\n   <span style=\"color: rgb(114,156,31)\">\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501<\/span> <span class=\"ansi-green-fg\">2\/2<\/span> [anthropic]\u2501 <span class=\"ansi-green-fg\">1\/2<\/span> [anthropic]\nSuccessfully installed anthropic-0.84.0 docstring-parser-0.17.0\n\n<span class=\"ansi-bold\">[<\/span><span class=\"ansi-blue-fg\">notice<\/span><span class=\"ansi-bold\">]<\/span> A new release of pip is available: <span class=\"ansi-red-fg\">25.3<\/span> -&gt; <span class=\"ansi-green-fg\">26.0.1<\/span>\n<span class=\"ansi-bold\">[<\/span><span class=\"ansi-blue-fg\">notice<\/span><span class=\"ansi-bold\">]<\/span> To update, run: <span class=\"ansi-green-fg\">python3 -m pip install --upgrade pip<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=0168a1a1-70e4-44d9-8ac7-51403f4c05b5\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[81]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"kn\">import<\/span><span class=\"w\"> <\/span><span class=\"nn\">pandas<\/span><span class=\"w\"> <\/span><span class=\"k\">as<\/span><span class=\"w\"> <\/span><span class=\"nn\">pd<\/span>\n<span class=\"kn\">import<\/span><span class=\"w\"> <\/span><span class=\"nn\">matplotlib.pyplot<\/span><span class=\"w\"> <\/span><span class=\"k\">as<\/span><span class=\"w\"> <\/span><span class=\"nn\">plt<\/span>\n<span class=\"kn\">import<\/span><span class=\"w\"> <\/span><span class=\"nn\">base64<\/span>\n<span class=\"kn\">import<\/span><span class=\"w\"> <\/span><span class=\"nn\">os<\/span>\n<span class=\"kn\">import<\/span><span class=\"w\"> <\/span><span class=\"nn\">requests<\/span>\n<span class=\"kn\">import<\/span><span class=\"w\"> <\/span><span class=\"nn\">json<\/span>\n<span class=\"kn\">import<\/span><span class=\"w\"> <\/span><span class=\"nn\">re<\/span>\n\n<span class=\"kn\">from<\/span><span class=\"w\"> <\/span><span class=\"nn\">dotenv<\/span><span class=\"w\"> <\/span><span class=\"kn\">import<\/span> <span class=\"n\">load_dotenv<\/span>\n<span class=\"n\">load_dotenv<\/span><span class=\"p\">()<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[81]:<\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>True<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=431f0175-356e-4465-8eb2-f187ceac2787\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"Importing-the-Dataset\">Importing the Dataset<a class=\"anchor-link\" href=\"#Importing-the-Dataset\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=641207c4-c4a1-4993-85ba-7474fbb1ed93\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[82]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"n\">dataset<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pd<\/span><span class=\"o\">.<\/span><span class=\"n\">read_csv<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"appliance_usage_dataset.csv\"<\/span><span class=\"p\">,<\/span>\n                      <span class=\"n\">encoding<\/span> <span class=\"o\">=<\/span> <span class=\"s1\">'utf-8'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">dataset<\/span> <span class=\"o\">=<\/span> <span class=\"n\">dataset<\/span><span class=\"p\">[:<\/span><span class=\"mi\">10000<\/span><span class=\"p\">]<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">dataset<\/span><span class=\"o\">.<\/span><span class=\"n\">shape<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">dataset<\/span><span class=\"o\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">()<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>(10000, 10)\n<\/pre>\n<\/div>\n<\/div>\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[82]:<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/html\" tabindex=\"0\">\n<div>\n<style scoped=\"\">\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n<\/style>\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th><\/th>\n<th>timestamp<\/th>\n<th>house_id<\/th>\n<th>season<\/th>\n<th>festival<\/th>\n<th>ac<\/th>\n<th>fridge<\/th>\n<th>lights<\/th>\n<th>fans<\/th>\n<th>washing_machine<\/th>\n<th>tv<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>0<\/th>\n<td>2023-01-01 00:00:00<\/td>\n<td>House_1<\/td>\n<td>winter<\/td>\n<td>NaN<\/td>\n<td>0.25<\/td>\n<td>0.59<\/td>\n<td>0.42<\/td>\n<td>0.38<\/td>\n<td>0.15<\/td>\n<td>0.26<\/td>\n<\/tr>\n<tr>\n<th>1<\/th>\n<td>2023-01-01 01:00:00<\/td>\n<td>House_1<\/td>\n<td>winter<\/td>\n<td>NaN<\/td>\n<td>0.12<\/td>\n<td>0.56<\/td>\n<td>0.38<\/td>\n<td>0.41<\/td>\n<td>0.11<\/td>\n<td>0.59<\/td>\n<\/tr>\n<tr>\n<th>2<\/th>\n<td>2023-01-01 02:00:00<\/td>\n<td>House_1<\/td>\n<td>winter<\/td>\n<td>NaN<\/td>\n<td>0.43<\/td>\n<td>0.36<\/td>\n<td>0.25<\/td>\n<td>0.26<\/td>\n<td>0.19<\/td>\n<td>0.41<\/td>\n<\/tr>\n<tr>\n<th>3<\/th>\n<td>2023-01-01 03:00:00<\/td>\n<td>House_1<\/td>\n<td>winter<\/td>\n<td>NaN<\/td>\n<td>0.27<\/td>\n<td>0.39<\/td>\n<td>0.38<\/td>\n<td>0.24<\/td>\n<td>0.19<\/td>\n<td>0.35<\/td>\n<\/tr>\n<tr>\n<th>4<\/th>\n<td>2023-01-01 04:00:00<\/td>\n<td>House_1<\/td>\n<td>winter<\/td>\n<td>NaN<\/td>\n<td>0.28<\/td>\n<td>0.54<\/td>\n<td>0.26<\/td>\n<td>0.35<\/td>\n<td>0.28<\/td>\n<td>0.22<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=9e92b004-71e5-4031-ab52-c78b5878eabf\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"Inserting-Data-in-GridDB\">Inserting Data in GridDB<a class=\"anchor-link\" href=\"#Inserting-Data-in-GridDB\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=aa0116a9-c19c-44c8-887a-195fb407429e\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h3 id=\"Creating-a-GridDB-Connection\">Creating a GridDB Connection<a class=\"anchor-link\" href=\"#Creating-a-GridDB-Connection\">\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=d4b7e049-e183-4a66-91cc-5c309d2d547d\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[83]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"n\">username<\/span> <span class=\"o\">=<\/span> <span class=\"n\">os<\/span><span class=\"o\">.<\/span><span class=\"n\">environ<\/span><span class=\"o\">.<\/span><span class=\"n\">get<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"username\"<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">password<\/span> <span class=\"o\">=<\/span> <span class=\"n\">os<\/span><span class=\"o\">.<\/span><span class=\"n\">environ<\/span><span class=\"o\">.<\/span><span class=\"n\">get<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"password\"<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">base_url<\/span> <span class=\"o\">=<\/span> <span class=\"n\">os<\/span><span class=\"o\">.<\/span><span class=\"n\">environ<\/span><span class=\"o\">.<\/span><span class=\"n\">get<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"base_url\"<\/span><span class=\"p\">)<\/span>\n\n\n<span class=\"n\">url<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"si\">{<\/span><span class=\"n\">base_url<\/span><span class=\"si\">}<\/span><span class=\"s2\">\/checkConnection\"<\/span>\n\n<span class=\"n\">credentials<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"si\">{<\/span><span class=\"n\">username<\/span><span class=\"si\">}<\/span><span class=\"s2\">:<\/span><span class=\"si\">{<\/span><span class=\"n\">password<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span>\n<span class=\"n\">encoded_credentials<\/span> <span class=\"o\">=<\/span> <span class=\"n\">base64<\/span><span class=\"o\">.<\/span><span class=\"n\">b64encode<\/span><span class=\"p\">(<\/span><span class=\"n\">credentials<\/span><span class=\"o\">.<\/span><span class=\"n\">encode<\/span><span class=\"p\">())<\/span><span class=\"o\">.<\/span><span class=\"n\">decode<\/span><span class=\"p\">()<\/span>\n\n<span class=\"n\">headers<\/span> <span class=\"o\">=<\/span> <span class=\"p\">{<\/span>\n    <span class=\"s1\">'Content-Type'<\/span><span class=\"p\">:<\/span> <span class=\"s1\">'application\/json'<\/span><span class=\"p\">,<\/span>  <span class=\"c1\"># Added this header to specify JSON content<\/span>\n    <span class=\"s1\">'Authorization'<\/span><span class=\"p\">:<\/span> <span class=\"sa\">f<\/span><span class=\"s1\">'Basic <\/span><span class=\"si\">{<\/span><span class=\"n\">encoded_credentials<\/span><span class=\"si\">}<\/span><span class=\"s1\">'<\/span><span class=\"p\">,<\/span>\n    <span class=\"s1\">'User-Agent'<\/span><span class=\"p\">:<\/span> <span class=\"s1\">'PostmanRuntime\/7.29.0'<\/span>\n<span class=\"p\">}<\/span>\n\n<span class=\"n\">response<\/span> <span class=\"o\">=<\/span> <span class=\"n\">requests<\/span><span class=\"o\">.<\/span><span class=\"n\">get<\/span><span class=\"p\">(<\/span><span class=\"n\">url<\/span><span class=\"p\">,<\/span> <span class=\"n\">headers<\/span><span class=\"o\">=<\/span><span class=\"n\">headers<\/span><span class=\"p\">)<\/span>\n\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">status_code<\/span><span class=\"p\">)<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">text<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>200\n\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=400c5d66-3223-427f-b601-10d97ba2b2de\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h3 id=\"Creating-a-GridDB-Container-for-Electricity-Consumption-Data\">Creating a GridDB Container for Electricity Consumption Data<a class=\"anchor-link\" href=\"#Creating-a-GridDB-Container-for-Electricity-Consumption-Data\">\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=482f87c0-beb8-40f4-88c6-4674701f5fbe\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[84]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"n\">dataset<\/span><span class=\"o\">.<\/span><span class=\"n\">insert<\/span><span class=\"p\">(<\/span><span class=\"mi\">0<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"SerialNo\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">dataset<\/span><span class=\"o\">.<\/span><span class=\"n\">index<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">dataset<\/span><span class=\"o\">.<\/span><span class=\"n\">columns<\/span><span class=\"o\">.<\/span><span class=\"n\">name<\/span> <span class=\"o\">=<\/span> <span class=\"kc\">None<\/span>   \n<span class=\"c1\"># Mapping pandas dtypes to GridDB types<\/span>\n<span class=\"n\">type_mapping<\/span> <span class=\"o\">=<\/span> <span class=\"p\">{<\/span>\n    <span class=\"s2\">\"int64\"<\/span><span class=\"p\">:<\/span>          <span class=\"s2\">\"LONG\"<\/span><span class=\"p\">,<\/span>\n    <span class=\"s2\">\"float64\"<\/span><span class=\"p\">:<\/span>        <span class=\"s2\">\"DOUBLE\"<\/span><span class=\"p\">,<\/span>\n    <span class=\"s2\">\"bool\"<\/span><span class=\"p\">:<\/span>           <span class=\"s2\">\"BOOL\"<\/span><span class=\"p\">,<\/span>\n    <span class=\"s1\">'datetime64'<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"TIMESTAMP\"<\/span><span class=\"p\">,<\/span> \n    <span class=\"s2\">\"object\"<\/span><span class=\"p\">:<\/span>         <span class=\"s2\">\"STRING\"<\/span><span class=\"p\">,<\/span>\n    <span class=\"s2\">\"category\"<\/span><span class=\"p\">:<\/span>       <span class=\"s2\">\"STRING\"<\/span><span class=\"p\">,<\/span>\n<span class=\"p\">}<\/span>\n\n<span class=\"c1\"># Generate the columns part of the payload dynamically<\/span>\n<span class=\"n\">columns<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n<span class=\"k\">for<\/span> <span class=\"n\">col<\/span><span class=\"p\">,<\/span> <span class=\"n\">dtype<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">dataset<\/span><span class=\"o\">.<\/span><span class=\"n\">dtypes<\/span><span class=\"o\">.<\/span><span class=\"n\">items<\/span><span class=\"p\">():<\/span>\n    <span class=\"n\">griddb_type<\/span> <span class=\"o\">=<\/span> <span class=\"n\">type_mapping<\/span><span class=\"o\">.<\/span><span class=\"n\">get<\/span><span class=\"p\">(<\/span><span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">dtype<\/span><span class=\"p\">),<\/span> <span class=\"s2\">\"STRING\"<\/span><span class=\"p\">)<\/span>  <span class=\"c1\"># Default to STRING if unknown<\/span>\n    <span class=\"n\">columns<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">({<\/span>\n        <span class=\"s2\">\"name\"<\/span><span class=\"p\">:<\/span> <span class=\"n\">col<\/span><span class=\"p\">,<\/span>\n        <span class=\"s2\">\"type\"<\/span><span class=\"p\">:<\/span> <span class=\"n\">griddb_type<\/span>\n    <span class=\"p\">})<\/span>\n\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">columns<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>[{'name': 'SerialNo', 'type': 'LONG'}, {'name': 'timestamp', 'type': 'STRING'}, {'name': 'house_id', 'type': 'STRING'}, {'name': 'season', 'type': 'STRING'}, {'name': 'festival', 'type': 'STRING'}, {'name': 'ac', 'type': 'DOUBLE'}, {'name': 'fridge', 'type': 'DOUBLE'}, {'name': 'lights', 'type': 'DOUBLE'}, {'name': 'fans', 'type': 'DOUBLE'}, {'name': 'washing_machine', 'type': 'DOUBLE'}, {'name': 'tv', 'type': 'DOUBLE'}]\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=a6f6b2cf-6171-4780-843e-983c50ca28c0\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[85]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"n\">container_name<\/span> <span class=\"o\">=<\/span> <span class=\"s2\">\"electricity_consumption_db\"<\/span>\n\n<span class=\"n\">url<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"si\">{<\/span><span class=\"n\">base_url<\/span><span class=\"si\">}<\/span><span class=\"s2\">\/containers\"<\/span>\n\n<span class=\"c1\"># Create the payload for the POST request<\/span>\n<span class=\"n\">payload<\/span> <span class=\"o\">=<\/span> <span class=\"n\">json<\/span><span class=\"o\">.<\/span><span class=\"n\">dumps<\/span><span class=\"p\">({<\/span>\n    <span class=\"s2\">\"container_name\"<\/span><span class=\"p\">:<\/span> <span class=\"n\">container_name<\/span><span class=\"p\">,<\/span>\n    <span class=\"s2\">\"container_type\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"COLLECTION\"<\/span><span class=\"p\">,<\/span>\n    <span class=\"s2\">\"rowkey\"<\/span><span class=\"p\">:<\/span> <span class=\"kc\">True<\/span><span class=\"p\">,<\/span>  <span class=\"c1\"># Assuming the first column as rowkey<\/span>\n    <span class=\"s2\">\"columns\"<\/span><span class=\"p\">:<\/span> <span class=\"n\">columns<\/span>\n<span class=\"p\">})<\/span>\n\n\n<span class=\"c1\"># Make the POST request to create the container<\/span>\n<span class=\"n\">response<\/span> <span class=\"o\">=<\/span> <span class=\"n\">requests<\/span><span class=\"o\">.<\/span><span class=\"n\">post<\/span><span class=\"p\">(<\/span><span class=\"n\">url<\/span><span class=\"p\">,<\/span> <span class=\"n\">headers<\/span><span class=\"o\">=<\/span><span class=\"n\">headers<\/span><span class=\"p\">,<\/span> <span class=\"n\">data<\/span><span class=\"o\">=<\/span><span class=\"n\">payload<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Print the response<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Status Code: <\/span><span class=\"si\">{<\/span><span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">status_code<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>Status Code: 201\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=82f5d050-3381-410b-b2dc-f2b7c243ba41\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h3 id=\"Inserting-Data-in-Electricity-Consumption-GridDB-Container\">Inserting Data in Electricity Consumption GridDB Container<a class=\"anchor-link\" href=\"#Inserting-Data-in-Electricity-Consumption-GridDB-Container\">\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=b53d6c44-71c2-411a-a16b-9caa0567c988\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[86]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"n\">url<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"si\">{<\/span><span class=\"n\">base_url<\/span><span class=\"si\">}<\/span><span class=\"s2\">\/containers\/<\/span><span class=\"si\">{<\/span><span class=\"n\">container_name<\/span><span class=\"si\">}<\/span><span class=\"s2\">\/rows\"<\/span>\n<span class=\"c1\"># Convert dataset to list of lists (row-wise) with proper formatting<\/span>\n\n<span class=\"k\">def<\/span><span class=\"w\"> <\/span><span class=\"nf\">format_row<\/span><span class=\"p\">(<\/span><span class=\"n\">row<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">formatted<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">item<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">row<\/span><span class=\"p\">:<\/span>\n        <span class=\"k\">if<\/span> <span class=\"n\">pd<\/span><span class=\"o\">.<\/span><span class=\"n\">isna<\/span><span class=\"p\">(<\/span><span class=\"n\">item<\/span><span class=\"p\">):<\/span>\n            <span class=\"n\">formatted<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"kc\">None<\/span><span class=\"p\">)<\/span>  <span class=\"c1\"># Convert NaN to None<\/span>\n        <span class=\"k\">elif<\/span> <span class=\"nb\">isinstance<\/span><span class=\"p\">(<\/span><span class=\"n\">item<\/span><span class=\"p\">,<\/span> <span class=\"nb\">bool<\/span><span class=\"p\">):<\/span>\n            <span class=\"n\">formatted<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">item<\/span><span class=\"p\">)<\/span><span class=\"o\">.<\/span><span class=\"n\">lower<\/span><span class=\"p\">())<\/span>  <span class=\"c1\"># Convert True\/False to true\/false<\/span>\n        <span class=\"k\">elif<\/span> <span class=\"nb\">isinstance<\/span><span class=\"p\">(<\/span><span class=\"n\">item<\/span><span class=\"p\">,<\/span> <span class=\"p\">(<\/span><span class=\"nb\">int<\/span><span class=\"p\">,<\/span> <span class=\"nb\">float<\/span><span class=\"p\">)):<\/span>\n            <span class=\"n\">formatted<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">item<\/span><span class=\"p\">)<\/span>  <span class=\"c1\"># Keep integers and floats as they are<\/span>\n        <span class=\"k\">else<\/span><span class=\"p\">:<\/span>\n            <span class=\"n\">formatted<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">item<\/span><span class=\"p\">))<\/span>  <span class=\"c1\"># Convert other types to string<\/span>\n    <span class=\"k\">return<\/span> <span class=\"n\">formatted<\/span>\n\n<span class=\"c1\"># Prepare rows with correct formatting<\/span>\n<span class=\"n\">rows<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"n\">format_row<\/span><span class=\"p\">(<\/span><span class=\"n\">row<\/span><span class=\"p\">)<\/span> <span class=\"k\">for<\/span> <span class=\"n\">row<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">dataset<\/span><span class=\"o\">.<\/span><span class=\"n\">values<\/span><span class=\"o\">.<\/span><span class=\"n\">tolist<\/span><span class=\"p\">()]<\/span>\n\n<span class=\"c1\"># Create payload as a JSON string<\/span>\n<span class=\"n\">payload<\/span> <span class=\"o\">=<\/span> <span class=\"n\">json<\/span><span class=\"o\">.<\/span><span class=\"n\">dumps<\/span><span class=\"p\">(<\/span><span class=\"n\">rows<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Make the PUT request to add the rows to the container<\/span>\n<span class=\"n\">response<\/span> <span class=\"o\">=<\/span> <span class=\"n\">requests<\/span><span class=\"o\">.<\/span><span class=\"n\">put<\/span><span class=\"p\">(<\/span><span class=\"n\">url<\/span><span class=\"p\">,<\/span> <span class=\"n\">headers<\/span><span class=\"o\">=<\/span><span class=\"n\">headers<\/span><span class=\"p\">,<\/span> <span class=\"n\">data<\/span><span class=\"o\">=<\/span><span class=\"n\">payload<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Print the response<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Status Code: <\/span><span class=\"si\">{<\/span><span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">status_code<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Response Text: <\/span><span class=\"si\">{<\/span><span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">text<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>Status Code: 200\nResponse Text: {\"count\":10000}\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=357c48e1-c892-420d-8c0d-fa84a5ca1916\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"Using-AI-to-Monitor-Electricity-Consumption-and-Get-Recommendations\">Using AI to Monitor Electricity Consumption and Get Recommendations<a class=\"anchor-link\" href=\"#Using-AI-to-Monitor-Electricity-Consumption-and-Get-Recommendations\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=f9c06d93-28ba-4c97-b5ce-44ac92b405c7\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h3 id=\"Retrieve-Data-from-GridDB\">Retrieve Data from GridDB<a class=\"anchor-link\" href=\"#Retrieve-Data-from-GridDB\">\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=d27a11e3-98a4-41c4-8d53-7e1e587d8e61\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[87]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"n\">url<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"si\">{<\/span><span class=\"n\">base_url<\/span><span class=\"si\">}<\/span><span class=\"s2\">\/containers\/<\/span><span class=\"si\">{<\/span><span class=\"n\">container_name<\/span><span class=\"si\">}<\/span><span class=\"s2\">\/rows\"<\/span>\n\n<span class=\"c1\"># Define the payload for the query<\/span>\n<span class=\"n\">payload<\/span> <span class=\"o\">=<\/span> <span class=\"n\">json<\/span><span class=\"o\">.<\/span><span class=\"n\">dumps<\/span><span class=\"p\">({<\/span>\n    <span class=\"s2\">\"offset\"<\/span><span class=\"p\">:<\/span> <span class=\"mi\">0<\/span><span class=\"p\">,<\/span>           <span class=\"c1\"># Start from the first row<\/span>\n    <span class=\"s2\">\"limit\"<\/span><span class=\"p\">:<\/span> <span class=\"mi\">10000<\/span><span class=\"p\">,<\/span>         <span class=\"c1\"># Limit the number of rows returned<\/span>\n    <span class=\"s2\">\"condition\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"\"<\/span><span class=\"p\">,<\/span>       <span class=\"c1\"># No filtering condition (you can customize it)<\/span>\n    <span class=\"s2\">\"sort\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"\"<\/span>             <span class=\"c1\"># No sorting (you can customize it)<\/span>\n<span class=\"p\">})<\/span>\n\n<span class=\"c1\"># Make the POST request to read data from the container<\/span>\n<span class=\"n\">response<\/span> <span class=\"o\">=<\/span> <span class=\"n\">requests<\/span><span class=\"o\">.<\/span><span class=\"n\">post<\/span><span class=\"p\">(<\/span><span class=\"n\">url<\/span><span class=\"p\">,<\/span> <span class=\"n\">headers<\/span><span class=\"o\">=<\/span><span class=\"n\">headers<\/span><span class=\"p\">,<\/span> <span class=\"n\">data<\/span><span class=\"o\">=<\/span><span class=\"n\">payload<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Check response status and print output<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Status Code: <\/span><span class=\"si\">{<\/span><span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">status_code<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n<span class=\"k\">if<\/span> <span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">status_code<\/span> <span class=\"o\">==<\/span> <span class=\"mi\">200<\/span><span class=\"p\">:<\/span>\n    <span class=\"k\">try<\/span><span class=\"p\">:<\/span>\n        <span class=\"n\">data<\/span> <span class=\"o\">=<\/span> <span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">json<\/span><span class=\"p\">()<\/span>\n        <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Data retrieved successfully!\"<\/span><span class=\"p\">)<\/span>\n\n        <span class=\"c1\"># Convert the response to a DataFrame<\/span>\n        <span class=\"n\">rows<\/span> <span class=\"o\">=<\/span> <span class=\"n\">data<\/span><span class=\"o\">.<\/span><span class=\"n\">get<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"rows\"<\/span><span class=\"p\">,<\/span> <span class=\"p\">[])<\/span>\n        <span class=\"n\">electricity_consumption_db<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pd<\/span><span class=\"o\">.<\/span><span class=\"n\">DataFrame<\/span><span class=\"p\">(<\/span><span class=\"n\">rows<\/span><span class=\"p\">,<\/span> <span class=\"n\">columns<\/span><span class=\"o\">=<\/span><span class=\"p\">[<\/span><span class=\"n\">col<\/span> <span class=\"k\">for<\/span> <span class=\"n\">col<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">dataset<\/span><span class=\"o\">.<\/span><span class=\"n\">columns<\/span><span class=\"p\">])<\/span>\n\n    <span class=\"k\">except<\/span> <span class=\"n\">json<\/span><span class=\"o\">.<\/span><span class=\"n\">JSONDecodeError<\/span><span class=\"p\">:<\/span>\n        <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Error: Failed to decode JSON response.\"<\/span><span class=\"p\">)<\/span>\n<span class=\"k\">else<\/span><span class=\"p\">:<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Error: Failed to query data from the container. Response: <\/span><span class=\"si\">{<\/span><span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">text<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">electricity_consumption_db<\/span><span class=\"o\">.<\/span><span class=\"n\">shape<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">electricity_consumption_db<\/span><span class=\"o\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">()<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>Status Code: 200\nData retrieved successfully!\n(10000, 11)\n<\/pre>\n<\/div>\n<\/div>\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[87]:<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/html\" tabindex=\"0\">\n<div>\n<style scoped=\"\">\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n<\/style>\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th><\/th>\n<th>SerialNo<\/th>\n<th>timestamp<\/th>\n<th>house_id<\/th>\n<th>season<\/th>\n<th>festival<\/th>\n<th>ac<\/th>\n<th>fridge<\/th>\n<th>lights<\/th>\n<th>fans<\/th>\n<th>washing_machine<\/th>\n<th>tv<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>0<\/th>\n<td>1<\/td>\n<td>2023-01-01 00:00:00<\/td>\n<td>House_1<\/td>\n<td>winter<\/td>\n<td>None<\/td>\n<td>0.25<\/td>\n<td>0.59<\/td>\n<td>0.42<\/td>\n<td>0.38<\/td>\n<td>0.15<\/td>\n<td>0.26<\/td>\n<\/tr>\n<tr>\n<th>1<\/th>\n<td>2<\/td>\n<td>2023-01-01 01:00:00<\/td>\n<td>House_1<\/td>\n<td>winter<\/td>\n<td>None<\/td>\n<td>0.12<\/td>\n<td>0.56<\/td>\n<td>0.38<\/td>\n<td>0.41<\/td>\n<td>0.11<\/td>\n<td>0.59<\/td>\n<\/tr>\n<tr>\n<th>2<\/th>\n<td>3<\/td>\n<td>2023-01-01 02:00:00<\/td>\n<td>House_1<\/td>\n<td>winter<\/td>\n<td>None<\/td>\n<td>0.43<\/td>\n<td>0.36<\/td>\n<td>0.25<\/td>\n<td>0.26<\/td>\n<td>0.19<\/td>\n<td>0.41<\/td>\n<\/tr>\n<tr>\n<th>3<\/th>\n<td>4<\/td>\n<td>2023-01-01 03:00:00<\/td>\n<td>House_1<\/td>\n<td>winter<\/td>\n<td>None<\/td>\n<td>0.27<\/td>\n<td>0.39<\/td>\n<td>0.38<\/td>\n<td>0.24<\/td>\n<td>0.19<\/td>\n<td>0.35<\/td>\n<\/tr>\n<tr>\n<th>4<\/th>\n<td>5<\/td>\n<td>2023-01-01 04:00:00<\/td>\n<td>House_1<\/td>\n<td>winter<\/td>\n<td>None<\/td>\n<td>0.28<\/td>\n<td>0.54<\/td>\n<td>0.26<\/td>\n<td>0.35<\/td>\n<td>0.28<\/td>\n<td>0.22<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=3ea89056-a346-4ea0-8b1a-35be7a71649b\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h3 id=\"Build-Dataset-Summary\">Build Dataset Summary<a class=\"anchor-link\" href=\"#Build-Dataset-Summary\">\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=c0b61b3d-091f-40dd-8976-b1da9f40d66d\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[88]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"kn\">import<\/span><span class=\"w\"> <\/span><span class=\"nn\">pandas<\/span><span class=\"w\"> <\/span><span class=\"k\">as<\/span><span class=\"w\"> <\/span><span class=\"nn\">pd<\/span>\n\n<span class=\"n\">APPLIANCE_COLS<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"s1\">'ac'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'fridge'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'lights'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'fans'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'washing_machine'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'tv'<\/span><span class=\"p\">]<\/span>\n\n<span class=\"c1\"># Prepare the dataframe<\/span>\n<span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"s1\">'timestamp'<\/span><span class=\"p\">]<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pd<\/span><span class=\"o\">.<\/span><span class=\"n\">to_datetime<\/span><span class=\"p\">(<\/span><span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"s1\">'timestamp'<\/span><span class=\"p\">])<\/span>\n<span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"s1\">'Hour'<\/span><span class=\"p\">]<\/span> <span class=\"o\">=<\/span> <span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"s1\">'timestamp'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">dt<\/span><span class=\"o\">.<\/span><span class=\"n\">hour<\/span>\n<span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"s1\">'DayOfWeek'<\/span><span class=\"p\">]<\/span> <span class=\"o\">=<\/span> <span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"s1\">'timestamp'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">dt<\/span><span class=\"o\">.<\/span><span class=\"n\">day_name<\/span><span class=\"p\">()<\/span>\n<span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"s1\">'Month'<\/span><span class=\"p\">]<\/span> <span class=\"o\">=<\/span> <span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"s1\">'timestamp'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">dt<\/span><span class=\"o\">.<\/span><span class=\"n\">month<\/span>\n<span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"s1\">'total_kwh'<\/span><span class=\"p\">]<\/span> <span class=\"o\">=<\/span> <span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"n\">APPLIANCE_COLS<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span><span class=\"n\">axis<\/span><span class=\"o\">=<\/span><span class=\"mi\">1<\/span><span class=\"p\">)<\/span>\n\n\n<span class=\"k\">def<\/span><span class=\"w\"> <\/span><span class=\"nf\">build_summary<\/span><span class=\"p\">(<\/span><span class=\"n\">df<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">parts<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n\n    <span class=\"c1\"># Overview<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Records: <\/span><span class=\"si\">{<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">df<\/span><span class=\"p\">)<\/span><span class=\"si\">}<\/span><span class=\"s2\"> hourly readings across <\/span><span class=\"si\">{<\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"s1\">'house_id'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">nunique<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\"> households\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Period: <\/span><span class=\"si\">{<\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"s1\">'timestamp'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">min<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\"> to <\/span><span class=\"si\">{<\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"s1\">'timestamp'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">max<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Seasons: <\/span><span class=\"si\">{<\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"s1\">'season'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">unique<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">tolist<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Festivals: <\/span><span class=\"si\">{<\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"s1\">'festival'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">unique<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">tolist<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"c1\"># Appliance stats<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">--- Appliance Stats (kWh) ---<\/span><span class=\"se\">\\n<\/span><span class=\"si\">{<\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"n\">APPLIANCE_COLS<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">describe<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">round<\/span><span class=\"p\">(<\/span><span class=\"mi\">3<\/span><span class=\"p\">)<\/span><span class=\"o\">.<\/span><span class=\"n\">to_string<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"c1\"># Total energy ranking<\/span>\n    <span class=\"n\">totals<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"n\">APPLIANCE_COLS<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">sort_values<\/span><span class=\"p\">(<\/span><span class=\"n\">ascending<\/span><span class=\"o\">=<\/span><span class=\"kc\">False<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">pct<\/span> <span class=\"o\">=<\/span> <span class=\"p\">(<\/span><span class=\"n\">totals<\/span> <span class=\"o\">\/<\/span> <span class=\"n\">totals<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">()<\/span> <span class=\"o\">*<\/span> <span class=\"mi\">100<\/span><span class=\"p\">)<\/span><span class=\"o\">.<\/span><span class=\"n\">round<\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">--- Energy Ranking ---\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">app<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">totals<\/span><span class=\"o\">.<\/span><span class=\"n\">index<\/span><span class=\"p\">:<\/span>\n        <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"  <\/span><span class=\"si\">{<\/span><span class=\"n\">app<\/span><span class=\"si\">}<\/span><span class=\"s2\">: <\/span><span class=\"si\">{<\/span><span class=\"n\">totals<\/span><span class=\"p\">[<\/span><span class=\"n\">app<\/span><span class=\"p\">]<\/span><span class=\"si\">:<\/span><span class=\"s2\">.1f<\/span><span class=\"si\">}<\/span><span class=\"s2\"> kWh (<\/span><span class=\"si\">{<\/span><span class=\"n\">pct<\/span><span class=\"p\">[<\/span><span class=\"n\">app<\/span><span class=\"p\">]<\/span><span class=\"si\">}<\/span><span class=\"s2\">%)\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"c1\"># Hourly patterns<\/span>\n    <span class=\"n\">hourly<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">groupby<\/span><span class=\"p\">(<\/span><span class=\"s1\">'Hour'<\/span><span class=\"p\">)[<\/span><span class=\"n\">APPLIANCE_COLS<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">mean<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">round<\/span><span class=\"p\">(<\/span><span class=\"mi\">4<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">hourly_total<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">groupby<\/span><span class=\"p\">(<\/span><span class=\"s1\">'Hour'<\/span><span class=\"p\">)[<\/span><span class=\"s1\">'total_kwh'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">mean<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">round<\/span><span class=\"p\">(<\/span><span class=\"mi\">2<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">--- Hourly Avg Consumption (kWh) ---<\/span><span class=\"se\">\\n<\/span><span class=\"si\">{<\/span><span class=\"n\">hourly<\/span><span class=\"o\">.<\/span><span class=\"n\">to_string<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">Hourly total avg:<\/span><span class=\"se\">\\n<\/span><span class=\"si\">{<\/span><span class=\"n\">hourly_total<\/span><span class=\"o\">.<\/span><span class=\"n\">to_string<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Lowest hour: <\/span><span class=\"si\">{<\/span><span class=\"n\">hourly_total<\/span><span class=\"o\">.<\/span><span class=\"n\">idxmin<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">:00 (<\/span><span class=\"si\">{<\/span><span class=\"n\">hourly_total<\/span><span class=\"o\">.<\/span><span class=\"n\">min<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\"> kWh)\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Highest hour: <\/span><span class=\"si\">{<\/span><span class=\"n\">hourly_total<\/span><span class=\"o\">.<\/span><span class=\"n\">idxmax<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">:00 (<\/span><span class=\"si\">{<\/span><span class=\"n\">hourly_total<\/span><span class=\"o\">.<\/span><span class=\"n\">max<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\"> kWh)\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"c1\"># Peak per appliance<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">--- Peak\/Low Hour per Appliance ---\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">col<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">APPLIANCE_COLS<\/span><span class=\"p\">:<\/span>\n        <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"  <\/span><span class=\"si\">{<\/span><span class=\"n\">col<\/span><span class=\"si\">}<\/span><span class=\"s2\">: peak <\/span><span class=\"si\">{<\/span><span class=\"n\">hourly<\/span><span class=\"p\">[<\/span><span class=\"n\">col<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">idxmax<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">:00 (<\/span><span class=\"si\">{<\/span><span class=\"n\">hourly<\/span><span class=\"p\">[<\/span><span class=\"n\">col<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">max<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">), low <\/span><span class=\"si\">{<\/span><span class=\"n\">hourly<\/span><span class=\"p\">[<\/span><span class=\"n\">col<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">idxmin<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">:00 (<\/span><span class=\"si\">{<\/span><span class=\"n\">hourly<\/span><span class=\"p\">[<\/span><span class=\"n\">col<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">min<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">)\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"c1\"># Season patterns<\/span>\n    <span class=\"n\">seasonal<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">groupby<\/span><span class=\"p\">(<\/span><span class=\"s1\">'season'<\/span><span class=\"p\">)[<\/span><span class=\"n\">APPLIANCE_COLS<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">mean<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">round<\/span><span class=\"p\">(<\/span><span class=\"mi\">4<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">--- Seasonal Avg (kWh) ---<\/span><span class=\"se\">\\n<\/span><span class=\"si\">{<\/span><span class=\"n\">seasonal<\/span><span class=\"o\">.<\/span><span class=\"n\">to_string<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"c1\"># Day of week<\/span>\n    <span class=\"n\">day_order<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"s1\">'Monday'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Tuesday'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Wednesday'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Thursday'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Friday'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Saturday'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Sunday'<\/span><span class=\"p\">]<\/span>\n    <span class=\"n\">daily<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">groupby<\/span><span class=\"p\">(<\/span><span class=\"s1\">'DayOfWeek'<\/span><span class=\"p\">)[<\/span><span class=\"s1\">'total_kwh'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">mean<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">reindex<\/span><span class=\"p\">(<\/span><span class=\"n\">day_order<\/span><span class=\"p\">)<\/span><span class=\"o\">.<\/span><span class=\"n\">round<\/span><span class=\"p\">(<\/span><span class=\"mi\">2<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">--- Day of Week Avg Total (kWh) ---<\/span><span class=\"se\">\\n<\/span><span class=\"si\">{<\/span><span class=\"n\">daily<\/span><span class=\"o\">.<\/span><span class=\"n\">to_string<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"c1\"># Household comparison (top 5 and bottom 5)<\/span>\n    <span class=\"n\">hh<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">groupby<\/span><span class=\"p\">(<\/span><span class=\"s1\">'house_id'<\/span><span class=\"p\">)[<\/span><span class=\"s1\">'total_kwh'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">mean<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">sort_values<\/span><span class=\"p\">(<\/span><span class=\"n\">ascending<\/span><span class=\"o\">=<\/span><span class=\"kc\">False<\/span><span class=\"p\">)<\/span><span class=\"o\">.<\/span><span class=\"n\">round<\/span><span class=\"p\">(<\/span><span class=\"mi\">2<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">--- Household Avg Hourly (kWh) ---\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Top 5:<\/span><span class=\"se\">\\n<\/span><span class=\"si\">{<\/span><span class=\"n\">hh<\/span><span class=\"o\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">to_string<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"Bottom 5:<\/span><span class=\"se\">\\n<\/span><span class=\"si\">{<\/span><span class=\"n\">hh<\/span><span class=\"o\">.<\/span><span class=\"n\">tail<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">to_string<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"c1\"># Festival impact<\/span>\n    <span class=\"n\">fest<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">groupby<\/span><span class=\"p\">(<\/span><span class=\"s1\">'festival'<\/span><span class=\"p\">)[<\/span><span class=\"s1\">'total_kwh'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">mean<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">round<\/span><span class=\"p\">(<\/span><span class=\"mi\">2<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">parts<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">--- Festival Impact (avg kWh) ---<\/span><span class=\"se\">\\n<\/span><span class=\"si\">{<\/span><span class=\"n\">fest<\/span><span class=\"o\">.<\/span><span class=\"n\">to_string<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"k\">return<\/span> <span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">\"<\/span><span class=\"o\">.<\/span><span class=\"n\">join<\/span><span class=\"p\">(<\/span><span class=\"n\">parts<\/span><span class=\"p\">)<\/span>\n\n\n<span class=\"n\">data_summary<\/span> <span class=\"o\">=<\/span> <span class=\"n\">build_summary<\/span><span class=\"p\">(<\/span><span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">)<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">data_summary<\/span><span class=\"p\">[:<\/span><span class=\"mi\">2000<\/span><span class=\"p\">])<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>Records: 10000 hourly readings across 1 households\nPeriod: 2023-01-01 00:00:00 to 2024-02-21 15:00:00\nSeasons: ['winter', 'summer', 'rainy', 'autumn']\nFestivals: [None, 'makar sankranti', 'mahashivratri', 'holi', 'ram navmi', 'ganesh chaturthi', 'navratri', 'dusshera', 'diwali', 'christmas']\n\n--- Appliance Stats (kWh) ---\n              ac     fridge     lights       fans  washing_machine         tv\ncount  10000.000  10000.000  10000.000  10000.000        10000.000  10000.000\nmean       0.469      0.449      0.373      0.449            0.349      0.400\nstd        0.341      0.087      0.176      0.210            0.210      0.116\nmin        0.100      0.300      0.200      0.200            0.100      0.200\n25%        0.230      0.370      0.280      0.300            0.200      0.300\n50%        0.360      0.450      0.350      0.390            0.290      0.400\n75%        0.480      0.520      0.430      0.490            0.390      0.500\nmax        1.490      0.600      1.650      1.090            1.000      0.600\n\n--- Energy Ranking ---\n  ac: 4689.3 kWh (18.8%)\n  fans: 4492.2 kWh (18.1%)\n  fridge: 4491.2 kWh (18.0%)\n  tv: 3998.6 kWh (16.1%)\n  lights: 3725.5 kWh (15.0%)\n  washing_machine: 3487.3 kWh (14.0%)\n\n--- Hourly Avg Consumption (kWh) ---\n          ac  fridge  lights    fans  washing_machine      tv\nHour                                                         \n0     0.4753  0.4503  0.3698  0.4484           0.3434  0.4014\n1     0.4693  0.4536  0.3732  0.4485           0.3411  0.3918\n2     0.4610  0.4479  0.3714  0.4539           0.3486  0.3988\n3     0.4641  0.4518  0.3688  0.4502           0.3423  0.4011\n4     0.4626  0.4468  0.3797  0.4478           0.3516  0.3900\n5     0.4676  0.4428  0.3837  0.4480           0.3495  0.4023\n6     0.4734  0.4408  0.3726  0.4446           0.3489  0.4031\n7     0.4636  0.4506  0.3629  0.4538           0.3490  0.3837\n8     0.4731  0.4542  0.3750  0.4511           0.3484  0.3941\n9     0.4703  0.4492  0.3693  0.4465           0.3488  0.\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=8ca1683c-ee75-41a1-91e2-ff5e31caec3e\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h3 id=\"Creating-Chatbot\">Creating Chatbot<a class=\"anchor-link\" href=\"#Creating-Chatbot\">\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=da4ef92b-8dd8-453a-85ab-ea9fd2272854\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[50]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"n\">SYSTEM_PROMPT<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">f<\/span><span class=\"s2\">\"\"\"You are an electricity consumption analyst. You have data from <\/span><span class=\"si\">{<\/span><span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">[<\/span><span class=\"s1\">'house_id'<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">nunique<\/span><span class=\"p\">()<\/span><span class=\"si\">}<\/span><span class=\"s2\"> households<\/span>\n<span class=\"s2\">with <\/span><span class=\"si\">{<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">electricity_consumption_db<\/span><span class=\"p\">)<\/span><span class=\"si\">}<\/span><span class=\"s2\"> hourly readings. Appliance columns: ac, fridge, lights, fans, washing_machine, tv.<\/span>\n<span class=\"s2\">All values are in kWh. The data also has season and festival columns.<\/span>\n\n<span class=\"s2\">Always cite specific numbers. Estimate kWh savings when giving recommendations.<\/span>\n\n<span class=\"s2\">DATA:<\/span>\n<span class=\"si\">{<\/span><span class=\"n\">data_summary<\/span><span class=\"si\">}<\/span>\n<span class=\"s2\">\"\"\"<\/span>\n\n\n<span class=\"k\">class<\/span><span class=\"w\"> <\/span><span class=\"nc\">ElectricityChatbot<\/span><span class=\"p\">:<\/span>\n    <span class=\"k\">def<\/span><span class=\"w\"> <\/span><span class=\"fm\">__init__<\/span><span class=\"p\">(<\/span><span class=\"bp\">self<\/span><span class=\"p\">):<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">client<\/span> <span class=\"o\">=<\/span> <span class=\"n\">anthropic<\/span><span class=\"o\">.<\/span><span class=\"n\">Anthropic<\/span><span class=\"p\">()<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">history<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n\n    <span class=\"k\">def<\/span><span class=\"w\"> <\/span><span class=\"nf\">ask<\/span><span class=\"p\">(<\/span><span class=\"bp\">self<\/span><span class=\"p\">,<\/span> <span class=\"n\">question<\/span><span class=\"p\">):<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">history<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">({<\/span><span class=\"s2\">\"role\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"user\"<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"content\"<\/span><span class=\"p\">:<\/span> <span class=\"n\">question<\/span><span class=\"p\">})<\/span>\n        <span class=\"n\">response<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">client<\/span><span class=\"o\">.<\/span><span class=\"n\">messages<\/span><span class=\"o\">.<\/span><span class=\"n\">create<\/span><span class=\"p\">(<\/span>\n            <span class=\"n\">model<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"claude-sonnet-4-6\"<\/span><span class=\"p\">,<\/span>\n            <span class=\"n\">max_tokens<\/span><span class=\"o\">=<\/span><span class=\"mi\">2048<\/span><span class=\"p\">,<\/span>\n            <span class=\"n\">system<\/span><span class=\"o\">=<\/span><span class=\"n\">SYSTEM_PROMPT<\/span><span class=\"p\">,<\/span>\n            <span class=\"n\">messages<\/span><span class=\"o\">=<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">history<\/span>\n        <span class=\"p\">)<\/span>\n        <span class=\"n\">answer<\/span> <span class=\"o\">=<\/span> <span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">content<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">text<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">history<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">({<\/span><span class=\"s2\">\"role\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"assistant\"<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"content\"<\/span><span class=\"p\">:<\/span> <span class=\"n\">answer<\/span><span class=\"p\">})<\/span>\n        <span class=\"k\">return<\/span> <span class=\"n\">answer<\/span>\n\n    <span class=\"k\">def<\/span><span class=\"w\"> <\/span><span class=\"nf\">reset<\/span><span class=\"p\">(<\/span><span class=\"bp\">self<\/span><span class=\"p\">):<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">history<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n        <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"History cleared.\"<\/span><span class=\"p\">)<\/span>\n\n\n<span class=\"n\">chatbot<\/span> <span class=\"o\">=<\/span> <span class=\"n\">ElectricityChatbot<\/span><span class=\"p\">()<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Chatbot ready!\"<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>Chatbot ready!\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=01302993-da92-4878-bb58-74cce5ed14de\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h3 id=\"Ask-Questions\">Ask Questions<a class=\"anchor-link\" href=\"#Ask-Questions\">\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=7bec3f6f-1d11-4831-a761-c764052f0d77\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[51]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"c1\"># Q1: Electricity trends<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">chatbot<\/span><span class=\"o\">.<\/span><span class=\"n\">ask<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"What are the main electricity consumption trends? Break down by appliance and season.\"<\/span><span class=\"p\">))<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre># Electricity Consumption Analysis: Trends &amp; Breakdown\n\n## \ud83d\udcca Overall Consumption Summary\n\nThe household consumes a **total of ~24,884 kWh** across the dataset period (Jan 2023 \u2013 Feb 2024), averaging **2.49 kWh\/hour** across all appliances combined.\n\n---\n\n## \ud83d\udd0c Appliance-by-Appliance Breakdown\n\n### 1. \ud83e\udd47 Air Conditioner \u2014 4,689 kWh (18.8%)\n- **Highest single consumer** in the household\n- Extreme variability: ranges from **0.10 to 1.49 kWh\/hour**\n- Standard deviation of **0.341** \u2014 the most volatile appliance\n- Peaks at **10:00 AM (0.481 kWh\/hr)**, lowest at **5:00 PM (0.458 kWh\/hr)**\n- Heavily season-dependent *(see seasonal breakdown below)*\n\n### 2. \ud83e\udd48 Fans \u2014 4,492 kWh (18.1%)\n- Nearly tied with fridge at **18.1% of total**\n- Peaks at **4:00 PM (0.456 kWh\/hr)**, lowest at **10:00 PM (0.444 kWh\/hr)**\n- Shows **dramatic rainy season spike** \u2014 almost doubles in usage\n- Moderate variability (std: **0.210**)\n\n### 3. \ud83e\udd49 Fridge \u2014 4,491 kWh (18.0%)\n- Most **consistent appliance**: std of only **0.087 kWh**\n- Ranges narrowly between **0.30 \u2013 0.60 kWh\/hr**\n- Peaks at **4:00 PM (0.459 kWh\/hr)**, slightly lower at **6:00 AM (0.441 kWh\/hr)**\n- Consumption stays stable at **~0.449 kWh\/hr** regardless of season (varies only \u00b10.006 across seasons)\n\n### 4. TV \u2014 3,999 kWh (16.1%)\n- Consistent usage throughout the day\n- Expected evening peak at **8:00 PM (0.415 kWh\/hr)**, lowest at **7:00 AM (0.384 kWh\/hr)**\n- Low variability (std: **0.116**), suggesting regular viewing habits\n- Minimal seasonal variation (**0.397\u20130.401 kWh\/hr** across all seasons)\n\n### 5. Lights \u2014 3,726 kWh (15.0%)\n- Surprisingly peaks at **5:00 AM (0.384 kWh\/hr)** \u2014 suggesting early morning activity\n- Lowest at **7:00 AM (0.363 kWh\/hr)**\n- Highest max of any appliance at **1.65 kWh\/hr** \u2014 festival lighting spikes likely driving this\n- Moderate variability (std: **0.176**)\n\n### 6. Washing Machine \u2014 3,487 kWh (14.0%)\n- **Lowest overall consumer** at 14% of total\n- Peaks at **noon \u2014 12:00 PM (0.357 kWh\/hr)**, lowest at **1:00 AM (0.341 kWh\/hr)**\n- Shows **massive rainy season anomaly** (see below)\n\n---\n\n## \ud83c\udf26\ufe0f Seasonal Breakdown\n\n| Appliance | Winter | Summer | Rainy | Autumn |\n|---|---|---|---|---|\n| **AC** | 0.304 kWh | **1.058 kWh** | 0.298 kWh | 0.303 kWh |\n| **Fans** | 0.348 kWh | 0.350 kWh | **0.800 kWh** | 0.352 kWh |\n| **Fridge** | 0.449 kWh | 0.453 kWh | 0.447 kWh | 0.448 kWh |\n| **Washing Machine** | 0.249 kWh | 0.250 kWh | **0.699 kWh** | 0.249 kWh |\n| **Lights** | 0.375 kWh | 0.374 kWh | 0.348 kWh | **0.392 kWh** |\n| **TV** | 0.401 kWh | 0.400 kWh | 0.399 kWh | 0.398 kWh |\n\n### Key Seasonal Findings:\n\n#### \u2600\ufe0f Summer \u2014 The AC Season\n- AC consumption **explodes to 1.058 kWh\/hr** \u2014 a **3.5\u00d7 increase** vs winter (0.304 kWh\/hr)\n- That's an extra **+0.754 kWh\/hr** purely from AC\n- All other appliances remain virtually unchanged in summer\n- **Summer AC is the single biggest seasonal driver** in the household\n\n#### \ud83c\udf27\ufe0f Rainy Season \u2014 The Fan &amp; Washing Machine Surge\n- Fans jump from ~0.350 kWh\/hr to **0.800 kWh\/hr (+129% increase)**\n- Washing machine nearly **triples**: 0.249 \u2192 **0.699 kWh\/hr (+181%)**\n- Combined rainy season surge adds approximately **+1.20 kWh\/hr** vs baseline\n- This likely reflects **higher humidity** driving fan use and more frequent washing of clothes\n- AC actually drops to its lowest (**0.298 kWh\/hr**) in rainy season\n\n#### \ud83c\udf42 Autumn \u2014 Light Usage Peaks\n- Lights are highest in autumn at **0.392 kWh\/hr** vs rainy season low of 0.348 kWh\/hr\n- Likely tied to festivals (Navratri, Dussehra, Diwali all fall in autumn)\n- All other appliances near their annual average\n\n#### \u2744\ufe0f Winter \u2014 Most Balanced Season\n- AC drops to **0.304 kWh\/hr** (near minimum)\n- No single appliance dominates\n- Total consumption is lowest and most evenly distributed\n- Most energy-efficient season overall\n\n---\n\n## \ud83d\udcc5 Day-of-Week Trends\n- Variation is **minimal** (2.46 kWh on Fridays to 2.51 kWh on Sundays)\n- **Sundays are the highest consumption day** (+0.05 kWh vs Friday) \u2014 household members likely at home\n- **Fridays are the lowest** at 2.46 kWh\/hr average\n\n---\n\n## \ud83c\udf89 Festival Impact \u2014 Significant Spikes\nAll festivals show consumption **well above the 2.49 kWh\/hr baseline**:\n\n| Festival | Avg kWh | Spike vs Normal |\n|---|---|---|\n| **Holi** | 3.92 kWh | **+57% above normal** |\n| **Ram Navami** | 3.77 kWh | **+51% above normal** |\n| **Mahashivratri** | 3.09 kWh | +24% |\n| **Dussehra** | 3.08 kWh | +24% |\n| **Diwali** | 3.07 kWh | +23% |\n\n- **Holi and Ram Navami** are outlier high-consumption festivals \u2014 likely driven by gatherings, cooking, and extended lighting\n- Even the lowest festival (Navratri at 3.03 kWh) is **+22% above the normal hourly average**\n\n---\n\n## \ud83d\udd11 Top 3 Takeaways\n1. **AC in summer** is the single largest controllable cost driver \u2014 managing it could yield the biggest savings\n2. **Rainy season** creates a unique double surge in fans + washing machine that is often overlooked\n3. **Fridge and TV** are essentially flat year-round \u2014 focus optimization efforts elsewhere\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=091e91ec-a8ad-4f08-b2b1-3e5a6aa819c2\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[52]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"c1\"># Q2: Savings recommendations<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">chatbot<\/span><span class=\"o\">.<\/span><span class=\"n\">ask<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Give me specific recommendations to save electricity with estimated kWh savings for each.\"<\/span><span class=\"p\">))<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre># Electricity Saving Recommendations with Estimated kWh Savings\n\n---\n\n## \ud83c\udfc6 Priority 1: High Impact (AC Optimization)\n\n### 1. \ud83c\udf21\ufe0f Raise AC Thermostat by 2\u00b0C in Summer\n**Current:** AC averages **1.058 kWh\/hr** in summer \u2014 the highest of any appliance\/season combo\n\n- Every 1\u00b0C increase in thermostat setting saves approximately **6% of AC energy**\n- Raising by 2\u00b0C = **~12% reduction** on summer AC consumption\n- Summer AC estimated hours: ~2,200 hrs (based on seasonal distribution)\n- **Estimated savings: ~278 kWh\/year**\n- At \u20b97\/unit: **~\u20b91,946\/year saved**\n\n---\n\n### 2. \u23f0 Shift AC Usage Away from 10 AM Peak\n**Current:** AC peaks at **10:00 AM (0.481 kWh\/hr)** vs daily low of **0.458 kWh\/hr at 5 PM**\n\n- Pre-cool rooms between 6\u20139 AM before peak heat builds\n- Reduces compressor strain during hottest hours\n- Difference of **0.023 kWh\/hr** across ~6 peak hours\/day\n- **Estimated savings: ~50 kWh\/year**\n- At \u20b97\/unit: **~\u20b9350\/year saved**\n\n---\n\n### 3. \ud83c\udf19 Set AC Auto-Shutoff at Night (Winter\/Autumn)\n**Current:** AC still runs at **0.303\u20130.304 kWh\/hr** during winter and autumn nights when it is largely unnecessary\n\n- AC runs 24 hrs in all seasons \u2014 nighttime winter AC (say 11 PM\u20136 AM, 7 hrs) can be eliminated\n- Winter + Autumn = ~6 months combined\n- Potential off hours: **7 hrs\/night \u00d7 180 nights = 1,260 hrs**\n- Savings: **1,260 \u00d7 0.304 = ~383 kWh\/year**\n- At \u20b97\/unit: **~\u20b92,681\/year saved**\n\n&gt; \ud83d\udca1 **Total AC Savings Potential: ~711 kWh\/year (~\u20b94,977\/year)**\n\n---\n\n## \ud83c\udfc6 Priority 2: Rainy Season \u2014 Fan &amp; Washing Machine Surge\n\n### 4. \ud83c\udf27\ufe0f Install Humidity-Controlled Fan Regulators\n**Current:** Fans spike from **0.350 kWh\/hr baseline to 0.800 kWh\/hr** in rainy season \u2014 a **+0.450 kWh\/hr surge**\n\n- Smart fan regulators reduce speed automatically when humidity is tolerable\n- Even a **20% reduction** during rainy season fan usage\n- Rainy season \u2248 3 months \u2248 2,160 hours\n- Savings: **2,160 \u00d7 0.450 \u00d7 0.20 = ~194 kWh**\n- At \u20b97\/unit: **~\u20b91,360\/year saved**\n\n---\n\n### 5. \ud83e\uddfa Reduce Washing Machine Run Frequency in Rainy Season\n**Current:** Washing machine nearly **triples** from 0.249 \u2192 **0.699 kWh\/hr** in rainy season (+0.450 kWh\/hr)\n\n- Rainy season likely causes more frequent washing due to damp clothes\n- Consolidating loads (reducing cycles by 25%) and using cold water settings\n- Rainy season \u2248 2,160 hours of elevated usage\n- Savings: **2,160 \u00d7 0.450 \u00d7 0.25 = ~243 kWh**\n- At \u20b97\/unit: **~\u20b91,701\/year saved**\n\n### 6. \u23f3 Shift Washing Machine to Off-Peak Hours\n**Current:** Washing machine peaks at **12:00 PM (0.357 kWh\/hr)**, lowest at **1:00 AM (0.341 kWh\/hr)**\n\n- Shifting loads to early morning (1\u20135 AM) or late night avoids peak grid load\n- Difference of **0.016 kWh\/hr** per cycle\n- Assuming 2 loads\/day \u00d7 365 days \u00d7 1 hr average cycle\n- **Estimated savings: ~12 kWh\/year**\n- Primarily benefits grid load management + potential ToD tariff savings\n\n&gt; \ud83d\udca1 **Total Rainy Season Savings Potential: ~449 kWh\/year (~\u20b93,143\/year)**\n\n---\n\n## \ud83c\udfc6 Priority 3: Lighting Optimization\n\n### 7. \ud83d\udca1 Replace High-Consumption Lights (Max Recorded: 1.65 kWh\/hr!)\n**Current:** Lights average **0.373 kWh\/hr** but spike to **1.65 kWh\/hr** at maximum\n\n- The max of 1.65 kWh\/hr suggests incandescent or halogen fixtures still in use\n- Switching entirely to LED reduces lighting load by **~60%**\n- Current annual lighting: **3,726 kWh total**\n- If even 30% of usage is non-LED: 3,726 \u00d7 0.30 \u00d7 0.60 = **~670 kWh\/year savings**\n- At \u20b97\/unit: **~\u20b94,690\/year saved**\n\n---\n\n### 8. \ud83c\udf05 Address the Unusual 5 AM Lighting Peak\n**Current:** Lights peak at **5:00 AM (0.384 kWh\/hr)** \u2014 higher than evening hours\n\n- This is abnormal \u2014 lighting should peak in evenings, not pre-dawn\n- Suggests lights are being left ON overnight (11 PM \u2192 5 AM)\n- Installing motion sensors or auto-timers for overnight hours\n- Potential overnight waste: **0.384 kWh\/hr \u00d7 6 hrs \u00d7 365 days = ~841 kWh\/year**\n- Even eliminating **50%** of unnecessary overnight lighting:\n- **Estimated savings: ~420 kWh\/year**\n- At \u20b97\/unit: **~\u20b92,940\/year saved**\n\n&gt; \ud83d\udca1 **Total Lighting Savings Potential: ~1,090 kWh\/year (~\u20b97,630\/year)**\n\n---\n\n## \ud83c\udfc6 Priority 4: TV &amp; Standby Power\n\n### 9. \ud83d\udcfa Eliminate TV Standby\/Idle Consumption\n**Current:** TV consumes **0.384 kWh\/hr even at its lowest (7 AM)** \u2014 suggesting standby draw\n\n- TV minimum of 0.384 kWh\/hr at 7 AM is unexpectedly high for standby\n- Using smart power strips to fully cut standby power\n- If standby accounts for **0.050 kWh\/hr** for ~8 idle hrs\/day:\n- **Estimated savings: 0.050 \u00d7 8 \u00d7 365 = ~146 kWh\/year**\n- At \u20b97\/unit: **~\u20b91,022\/year saved**\n\n---\n\n### 10. \u23f1\ufe0f Set TV Auto-Sleep Timer\n**Current:** TV peaks at **8 PM (0.415 kWh\/hr)** and stays elevated through late night\n\n- Setting a 2-hour auto-sleep timer reduces late-night passive consumption\n- Saves approximately **0.415 kWh\/hr \u00d7 1.5 hrs\/night \u00d7 365 = ~227 kWh\/year**\n- Assuming timer actually cuts 40% of that late-night usage:\n- **Estimated savings: ~91 kWh\/year**\n- At \u20b97\/unit: **~\u20b9637\/year saved**\n\n&gt; \ud83d\udca1 **Total TV Savings Potential: ~237 kWh\/year (~\u20b91,659\/year)**\n\n---\n\n## \ud83c\udfc6 Priority 5: Festival Period Management\n\n### 11. \ud83c\udf86 Cap Festival Lighting Duration (Especially Holi &amp; Ram Navami)\n**Current:** Holi hits **3.92 kWh\/hr** and Ram Navami **3.77 kWh\/hr** vs normal **2.49 kWh\/hr**\n\n- Holi excess: **3.92 \u2212 2.49 = +1.43 kWh\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=88374d5d-02b7-44cd-9fe4-0da8f6b866d0\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[53]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"c1\"># Q3: Follow-up<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">chatbot<\/span><span class=\"o\">.<\/span><span class=\"n\">ask<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Which single change gives the biggest savings with least effort?\"<\/span><span class=\"p\">))<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre># The Single Best Change: Fix the Overnight Lighting Issue\n\n## \ud83c\udfaf The Recommendation: Install Auto-Timers \/ Motion Sensors on Lights\n\n---\n\n## Why This Wins on Both Dimensions\n\n### \ud83d\udca1 The Smoking Gun in Your Data\n\nYour lights show a **deeply abnormal consumption pattern:**\n\n| Time | Lighting Load | Expected Behavior |\n|---|---|---|\n| **5:00 AM** | **0.384 kWh\/hr** \u2190 PEAK | Should be near zero |\n| **7:00 AM** | 0.363 kWh\/hr \u2190 LOW | Should still be low |\n| **8:00 PM** | 0.375 kWh\/hr | Should be the PEAK |\n\n&gt; **Lights are burning brightest at 5 AM \u2014 when almost nobody is awake.**\n&gt; This is the clearest sign of waste in your entire dataset.\n\n---\n\n## \ud83d\udcd0 The Numbers\n\n### Overnight Waste Calculation\n```\nUnnecessary overnight lighting:\n\u2192 0.384 kWh\/hr \u00d7 6 hours (11 PM\u20135 AM) \u00d7 365 nights\n\u2192 = 841 kWh\/year being burned while household sleeps\n\nConservative 50% recovery with auto-timers:\n\u2192 841 \u00d7 0.50 = ~420 kWh\/year saved\n\u2192 At \u20b97\/unit = ~\u20b92,940\/year saved\n```\n\n### Context: How Big Is This?\n- 420 kWh saved = **11.3% of your total annual lighting bill (3,726 kWh)**\n- Equivalent to running your fridge for **~935 hours**\n- Larger than the **entire daily consumption** of the household (~2.49 kWh\/hr \u00d7 24 = 59.8 kWh\/day \u2192 420 kWh = 7 full days of electricity)\n\n---\n\n## \u26a1 Why It Scores Highest on Least Effort\n\n### Effort Required: VERY LOW\n| Factor | Detail |\n|---|---|\n| **Cost of fix** | Smart timer plug: \u20b9300\u2013\u20b9800 one time |\n| **Installation time** | 5\u201310 minutes, no electrician needed |\n| **Behavior change required** | Zero \u2014 timers work automatically |\n| **Ongoing effort** | None after initial setup |\n| **Payback period** | **Less than 2 months** at \u20b92,940\/year savings |\n\n### Compare to Other High-Saving Options:\n| Recommendation | Annual Saving | Effort Level |\n|---|---|---|\n| **\ud83c\udfc6 Overnight light timers** | **~420 kWh** | **Very Low \u2014 set once** |\n| AC nighttime shutoff (winter) | ~383 kWh | Medium \u2014 behavior change |\n| Raise AC thermostat 2\u00b0C | ~278 kWh | Medium \u2014 comfort adjustment |\n| Replace lights with LED | ~670 kWh | High \u2014 upfront cost + installation |\n| Washing machine consolidation | ~243 kWh | Medium \u2014 daily habit change |\n\n&gt; LED replacement saves more \u2014 but costs \u20b95,000\u201315,000 upfront and requires\n&gt; replacing multiple fixtures. The timer costs \u20b9500 and takes 10 minutes.\n\n---\n\n## \ud83d\udee0\ufe0f Exactly What To Do\n\n### Step 1: Tonight\n- Walk through the house at midnight and note which lights are still ON\n- Check if any outdoor, corridor, or bathroom lights stay on permanently\n\n### Step 2: This Week (\u20b9500 fix)\n- Buy a **basic 24-hour mechanical timer switch** (available on Amazon\/local hardware store)\n- Set it to **cut all non-essential lights from 11:00 PM to 5:30 AM**\n- Alternatively install a **motion sensor** for bathroom\/corridor lights\n\n### Step 3: Optional Upgrade (\u20b91,500 fix)\n- Replace timer with a **smart WiFi plug** (Mi\/Wipro\/Syska brands)\n- Lets you schedule from phone + monitor actual consumption\n- Gives you real data to verify the savings\n\n---\n\n## \ud83d\udcca 12-Month Savings Projection\n\n```\nMonth 1\u20132:   Timer cost recovered (\u20b9500 investment)\nMonth 3\u201312:  Pure savings accumulate\nYear 1 net:  ~\u20b92,440 saved (after \u20b9500 hardware cost)\nYear 2+:     Full \u20b92,940\/year \u2014 zero additional cost\n5-Year total: ~\u20b914,200 saved from one 10-minute fix\n```\n\n---\n\n## \ud83d\udd11 Bottom Line\n\n&gt; **Your lights are at their PEAK consumption at 5 AM \u2014 a time when your\n&gt; household should be asleep. This single anomaly costs you ~\u20b92,940\/year.\n&gt; A \u20b9500 timer plug, set up in 10 minutes, recovers ~420 kWh\/year\n&gt; with absolutely zero ongoing effort or lifestyle change.**\n\nNo other recommendation in your data comes close to matching this\n**savings-to-effort ratio.**\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\" id=\"cell-id=2c2113fb-4c48-4b92-9f6f-83dd2eaa870d\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h3 id=\"Execute-Code-and-Generate-Charts\">Execute Code and Generate Charts<a class=\"anchor-link\" href=\"#Execute-Code-and-Generate-Charts\">\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\" id=\"cell-id=711c3b02-b83c-4bb9-acc5-c1f06bca79d7\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[70]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"k\">def<\/span><span class=\"w\"> <\/span><span class=\"nf\">generate_and_run_chart<\/span><span class=\"p\">(<\/span><span class=\"n\">question<\/span><span class=\"p\">):<\/span>\n<span class=\"w\">    <\/span><span class=\"sd\">\"\"\"<\/span>\n<span class=\"sd\">    Ask Claude to generate matplotlib code for a chart, then execute it.<\/span>\n<span class=\"sd\">    The chart displays inline in the notebook.<\/span>\n<span class=\"sd\">    \"\"\"<\/span>\n    <span class=\"n\">schema<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">f<\/span><span class=\"s2\">\"Columns: <\/span><span class=\"si\">{<\/span><span class=\"nb\">list<\/span><span class=\"p\">(<\/span><span class=\"n\">electricity_consumption_db<\/span><span class=\"o\">.<\/span><span class=\"n\">columns<\/span><span class=\"p\">)<\/span><span class=\"si\">}<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">Appliance cols: <\/span><span class=\"si\">{<\/span><span class=\"n\">APPLIANCE_COLS<\/span><span class=\"si\">}<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">Sample:<\/span><span class=\"se\">\\n<\/span><span class=\"si\">{<\/span><span class=\"n\">electricity_consumption_db<\/span><span class=\"o\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">(<\/span><span class=\"mi\">3<\/span><span class=\"p\">)<\/span><span class=\"o\">.<\/span><span class=\"n\">to_csv<\/span><span class=\"p\">(<\/span><span class=\"n\">index<\/span><span class=\"o\">=<\/span><span class=\"kc\">False<\/span><span class=\"p\">)<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span>\n\n    <span class=\"n\">response<\/span> <span class=\"o\">=<\/span> <span class=\"n\">client<\/span><span class=\"o\">.<\/span><span class=\"n\">messages<\/span><span class=\"o\">.<\/span><span class=\"n\">create<\/span><span class=\"p\">(<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"claude-sonnet-4-6\"<\/span><span class=\"p\">,<\/span>\n        <span class=\"n\">max_tokens<\/span><span class=\"o\">=<\/span><span class=\"mi\">2048<\/span><span class=\"p\">,<\/span>\n        <span class=\"n\">messages<\/span><span class=\"o\">=<\/span><span class=\"p\">[{<\/span>\n            <span class=\"s2\">\"role\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"user\"<\/span><span class=\"p\">,<\/span>\n            <span class=\"s2\">\"content\"<\/span><span class=\"p\">:<\/span> <span class=\"sa\">f<\/span><span class=\"s2\">\"\"\"I have a pandas DataFrame `electricity_consumption_db` with this structure:<\/span>\n\n<span class=\"si\">{<\/span><span class=\"n\">schema<\/span><span class=\"si\">}<\/span>\n\n<span class=\"s2\">The electricity_consumption_db already has 'Hour', 'DayOfWeek', 'Month', 'total_kwh' columns.<\/span>\n<span class=\"s2\">Appliance values (ac, fridge, lights, fans, washing_machine, tv) are in kWh.<\/span>\n\n<span class=\"s2\">Write Python code using pandas and matplotlib to: <\/span><span class=\"si\">{<\/span><span class=\"n\">question<\/span><span class=\"si\">}<\/span>\n\n<span class=\"s2\">RULES:<\/span>\n<span class=\"s2\">- Return ONLY Python code, no markdown fences, no explanation<\/span>\n<span class=\"s2\">- df is already loaded, matplotlib.pyplot is imported as plt<\/span>\n<span class=\"s2\">- Use plt.figure(figsize=(10, 6)) for good sizing<\/span>\n<span class=\"s2\">- Always include plt.tight_layout() and plt.show()<\/span>\n<span class=\"s2\">- Add clear title, axis labels, and legend where needed\"\"\"<\/span>\n        <span class=\"p\">}]<\/span>\n    <span class=\"p\">)<\/span>\n\n    <span class=\"n\">code<\/span> <span class=\"o\">=<\/span> <span class=\"n\">response<\/span><span class=\"o\">.<\/span><span class=\"n\">content<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">text<\/span>\n    <span class=\"c1\"># Strip markdown fences if Claude includes them<\/span>\n    <span class=\"n\">code<\/span> <span class=\"o\">=<\/span> <span class=\"n\">re<\/span><span class=\"o\">.<\/span><span class=\"n\">sub<\/span><span class=\"p\">(<\/span><span class=\"sa\">r<\/span><span class=\"s1\">'^```python\\s*'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">''<\/span><span class=\"p\">,<\/span> <span class=\"n\">code<\/span><span class=\"p\">,<\/span> <span class=\"n\">flags<\/span><span class=\"o\">=<\/span><span class=\"n\">re<\/span><span class=\"o\">.<\/span><span class=\"n\">MULTILINE<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">code<\/span> <span class=\"o\">=<\/span> <span class=\"n\">re<\/span><span class=\"o\">.<\/span><span class=\"n\">sub<\/span><span class=\"p\">(<\/span><span class=\"sa\">r<\/span><span class=\"s1\">'^```\\s*$'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">''<\/span><span class=\"p\">,<\/span> <span class=\"n\">code<\/span><span class=\"p\">,<\/span> <span class=\"n\">flags<\/span><span class=\"o\">=<\/span><span class=\"n\">re<\/span><span class=\"o\">.<\/span><span class=\"n\">MULTILINE<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">code<\/span> <span class=\"o\">=<\/span> <span class=\"n\">code<\/span><span class=\"o\">.<\/span><span class=\"n\">strip<\/span><span class=\"p\">()<\/span>\n\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Generated code:\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"-\"<\/span> <span class=\"o\">*<\/span> <span class=\"mi\">40<\/span><span class=\"p\">)<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">code<\/span><span class=\"p\">)<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"-\"<\/span> <span class=\"o\">*<\/span> <span class=\"mi\">40<\/span><span class=\"p\">)<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">Executing...<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"n\">exec<\/span><span class=\"p\">(<\/span><span class=\"n\">code<\/span><span class=\"p\">,<\/span> <span class=\"nb\">globals<\/span><span class=\"p\">())<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=d4fffc59-4b25-4ef3-b25d-8cbd898fcceb\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[68]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"c1\"># Chart 1: Hourly consumption by appliance<\/span>\n<span class=\"n\">generate_and_run_chart<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Line chart showing average hourly consumption for each appliance, all on one plot with different colors and a legend.\"<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>Generated code:\n----------------------------------------\nappliance_cols = ['ac', 'fridge', 'lights', 'fans', 'washing_machine', 'tv']\n\nhourly_avg = electricity_consumption_db.groupby('Hour')[appliance_cols].mean()\n\nplt.figure(figsize=(10, 6))\n\nfor appliance in appliance_cols:\n    plt.plot(hourly_avg.index, hourly_avg[appliance], marker='o', label=appliance.replace('_', ' ').title())\n\nplt.title('Average Hourly Consumption by Appliance')\nplt.xlabel('Hour of Day')\nplt.ylabel('Average Consumption (kWh)')\nplt.xticks(range(0, 24))\nplt.legend(title='Appliance', bbox_to_anchor=(1.05, 1), loc='upper left')\nplt.grid(True, linestyle='--', alpha=0.7)\nplt.tight_layout()\nplt.show()\n----------------------------------------\n\nExecuting...\n\n<\/pre>\n<\/div>\n<\/div>\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedImage jp-OutputArea-output\" tabindex=\"0\">\n<img decoding=\"async\" alt=\"No description has been provided for this image\" class=\"\" 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\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\">\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=e36daade-75cb-4b46-9970-9600b82bae21\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[73]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"c1\"># Chart 2: Total energy by appliance<\/span>\n<span class=\"n\">generate_and_run_chart<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Bar chart showing total energy consumption per appliance, sorted highest to lowest, with kWh values on each bar.\"<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>Generated code:\n----------------------------------------\nappliance_cols = ['ac', 'fridge', 'lights', 'fans', 'washing_machine', 'tv']\n\nappliance_totals = electricity_consumption_db[appliance_cols].sum().sort_values(ascending=False)\n\nplt.figure(figsize=(10, 6))\n\nbars = plt.bar(appliance_totals.index, appliance_totals.values, color='steelblue', edgecolor='black')\n\nfor bar, value in zip(bars, appliance_totals.values):\n    plt.text(bar.get_x() + bar.get_width() \/ 2, bar.get_height() + max(appliance_totals.values) * 0.01,\n             f'{value:,.2f} kWh', ha='center', va='bottom', fontsize=10, fontweight='bold')\n\nplt.title('Total Energy Consumption per Appliance', fontsize=16, fontweight='bold', pad=15)\nplt.xlabel('Appliance', fontsize=13)\nplt.ylabel('Total Energy Consumption (kWh)', fontsize=13)\nplt.xticks(fontsize=11)\nplt.yticks(fontsize=11)\nplt.ylim(0, max(appliance_totals.values) * 1.12)\n\nplt.tight_layout()\nplt.show()\n----------------------------------------\n\nExecuting...\n\n<\/pre>\n<\/div>\n<\/div>\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedImage jp-OutputArea-output\" tabindex=\"0\">\n<img decoding=\"async\" alt=\"No description has been provided for this image\" class=\"\" 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\">\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\" id=\"cell-id=e39c8312-c1ac-4e0e-89a9-d18932bbe9ae\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[77]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span><span class=\"c1\"># Chart 3: Seasonal patterns<\/span>\n<span class=\"n\">generate_and_run_chart<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Bar chart showing daily average consumption of each device for all the seasons\"<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>Generated code:\n----------------------------------------\nappliance_cols = ['ac', 'fridge', 'lights', 'fans', 'washing_machine', 'tv']\n\ndaily_avg = electricity_consumption_db.copy()\ndaily_avg['date'] = pd.to_datetime(daily_avg['timestamp']).dt.date\ndaily_seasonal = daily_avg.groupby(['date', 'season'])[appliance_cols].sum().reset_index()\nseasonal_daily_avg = daily_seasonal.groupby('season')[appliance_cols].mean()\n\nseasons = seasonal_daily_avg.index.tolist()\nn_seasons = len(seasons)\nn_appliances = len(appliance_cols)\n\nimport numpy as np\nx = np.arange(n_seasons)\nbar_width = 0.12\noffsets = np.linspace(-(n_appliances - 1) \/ 2 * bar_width, (n_appliances - 1) \/ 2 * bar_width, n_appliances)\n\nplt.figure(figsize=(10, 6))\n\ncolors = ['#FF6B6B', '#4ECDC4', '#FFE66D', '#95E1D3', '#F38181', '#C9B1FF']\n\nfor i, (appliance, color, offset) in enumerate(zip(appliance_cols, colors, offsets)):\n    plt.bar(x + offset, seasonal_daily_avg[appliance], width=bar_width, label=appliance.replace('_', ' ').title(), color=color, edgecolor='black', linewidth=0.5)\n\nplt.title('Daily Average Consumption of Each Device by Season', fontsize=14, fontweight='bold')\nplt.xlabel('Season', fontsize=12)\nplt.ylabel('Average Daily Consumption (kWh)', fontsize=12)\nplt.xticks(x, [s.title() for s in seasons], fontsize=11)\nplt.legend(title='Appliance', bbox_to_anchor=(1.01, 1), loc='upper left', fontsize=10)\nplt.grid(axis='y', linestyle='--', alpha=0.6)\n\nplt.tight_layout()\nplt.show()\n----------------------------------------\n\nExecuting...\n\n<\/pre>\n<\/div>\n<\/div>\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedImage jp-OutputArea-output\" tabindex=\"0\">\n<img decoding=\"async\" alt=\"No description has been provided for this image\" class=\"\" 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class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\" id=\"cell-id=a658e6f6-e757-4b7b-8589-f8fd3411f290\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-ipython3\"><pre><span><\/span> \n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n    <\/div>\n    <div class=\"nbconvert-labels\">\n      <label class=\"github-link\">\n        <a href=\"https:\/\/github.com\/usmanmalik57\/GridDB-Blogs\/blob\/main\/Electricity%20Consumption%20Monitoring%20Chatbot%20with%20GridDB%20and%20Anthropic%20Claude\/Jupyter%20Notebook%20Codes.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\">Also available on Github<\/a>\n        <label class=\"github-last-update\"> Last updated: 29\/10\/2026 06:17:35<\/label>\n      <\/label>\n    <\/div>\n  <\/div>\n","protected":false},"excerpt":{"rendered":"<p>Monitoring household electricity consumption is essential for identifying wasteful patterns and reducing energy costs. However, manually analyzing thousands of hourly readings across multiple appliances is impractical. Combining AI-powered analysis with a high-performance database like GridDB allows you to store, retrieve, and analyze electricity data using natural language queries. In this article, you will see how [&hellip;]<\/p>\n","protected":false},"author":779,"featured_media":55670,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-55658","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Electricity Consumption Monitoring Chatbot with GridDB and Anthropic Claude | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"Monitoring household electricity consumption is essential for identifying wasteful patterns and reducing energy costs. 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