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  "format": "dataset-manifest/1",
  "slug": "ai-enhanced-home-energy-efficiency-analyzer",
  "version": 2,
  "seed": "ai-enhanced-home-energy-efficiency-analyzer:1",
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    "slug": "ai-enhanced-home-energy-efficiency-analyzer",
    "name": "Home Energy Optimization Metrics",
    "tagline": "Metrics on smart meters, appliance usage, solar installations, and electric vehicle data for home energy optimization.",
    "description": "Homes increasingly integrate smart technology to monitor and optimize energy efficiency. This dataset models the analysis of data from smart meters, appliances, solar installations, and electric vehicles, providing insights on energy usage patterns and potential savings. Users can explore energy consumption trends, identify spikes during peak hours, and evaluate solar energy generation on sunny days. Additionally, the dataset opens avenues for understanding demand response and energy storage management strategies, making it invaluable for analysts and decision-makers in the energy sector. The tables are synthetic, ensuring a variety of scenarios and metrics relevant to energy efficiency optimization.",
    "domain": "energy",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "energy efficiency",
      "smart home",
      "data analysis",
      "appliance usage",
      "solar generation",
      "electric vehicles",
      "demand response"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
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      "smart_meters": {
        "description": "This table captures data from smart meters installed in homes, linking to specific households and recording energy consumption over time.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each smart meter."
          },
          "home_id": {
            "kind": "fk",
            "ref": "homes.id",
            "description": "Reference to the home this meter belongs to."
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date and time when the reading was taken."
          },
          "energy_consumption_kwh": {
            "kind": "decimal",
            "min": 0,
            "max": 1000,
            "scale": 2,
            "description": "Energy consumption in kilowatt-hours.",
            "distribution": "normal"
          }
        }
      },
      "appliance_usage": {
        "description": "This table details the usage patterns of various appliances in linked homes, providing insights into energy consumption behavior.",
        "rows": 800,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each appliance usage record."
          },
          "home_id": {
            "kind": "fk",
            "ref": "homes.id",
            "description": "Reference to the home this appliance belongs to."
          },
          "appliance_type": {
            "kind": "enum",
            "values": [
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              "washing_machine",
              "air_conditioner",
              "heater",
              "tv"
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            "description": "Type of appliance being monitored."
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          "usage_hours": {
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            "min": 0,
            "max": 24,
            "description": "Number of hours the appliance is used in a day.",
            "nullable": true,
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                "tv"
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            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date and time of the usage record.",
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            "trend": "growing"
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      "solar_installations": {
        "description": "This table records the solar energy generation from installations at homes, highlighting output trends over time.",
        "rows": 400,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each solar installation record."
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            "kind": "fk",
            "ref": "homes.id",
            "description": "Reference to the home with the solar installation."
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            "to": "2023-12-31",
            "description": "The date and time when the solar generation was recorded.",
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            "min": 0,
            "max": 1000,
            "scale": 2,
            "description": "Energy generated by the solar installation in kilowatt-hours.",
            "distribution": "normal"
          }
        }
      },
      "electric_vehicles": {
        "description": "This table provides data on the energy consumption and charging patterns of electric vehicles linked to homes.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each electric vehicle record."
          },
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            "description": "The date and time of the charging or consumption record.",
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            "distribution": "normal"
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        "description": "This table lists the homes integrated with smart technology, including their identifiers and locations.",
        "rows": 200,
        "columns": {
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            "kind": "pk",
            "description": "Unique identifier for each home."
          },
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            "description": "Address of the home.",
            "nullable": true
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          "city": {
            "kind": "city",
            "description": "City where the home is located.",
            "nullable": true
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          "state": {
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            "min": 1,
            "max": 1,
            "description": "State where the home is located.",
            "nullable": true,
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              "Alaska",
              "Arizona",
              "Arkansas",
              "California",
              "Colorado",
              "Connecticut",
              "Delaware",
              "Florida",
              "Georgia",
              "Hawaii",
              "Idaho",
              "Illinois",
              "Indiana",
              "Iowa",
              "Kansas",
              "Kentucky",
              "Louisiana",
              "Maine",
              "Maryland",
              "Massachusetts",
              "Michigan",
              "Minnesota",
              "Mississippi",
              "Missouri",
              "Montana",
              "Nebraska",
              "Nevada",
              "NewHampshire",
              "NewJersey",
              "NewMexico",
              "NewYork",
              "NorthCarolina",
              "NorthDakota",
              "Ohio",
              "Oklahoma",
              "Oregon",
              "Pennsylvania",
              "RhodeIsland",
              "SouthCarolina",
              "SouthDakota",
              "Tennessee",
              "Texas",
              "Utah",
              "Vermont",
              "Virginia",
              "Washington",
              "WestVirginia",
              "Wisconsin",
              "Wyoming"
            ]
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          "zipcode": {
            "kind": "postcode",
            "description": "Postal code for the home.",
            "nullable": true
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        "description": "This table captures energy audit results for homes, providing insights into potential energy savings and efficiency measures.",
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        "columns": {
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            "description": "Unique identifier for each energy audit."
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            "nullable": true,
            "distribution": "long-tail"
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    "analysisQuestions": [
      "What are the peak hours of energy consumption for different homes?",
      "How does solar energy generation correlate with sunny days?",
      "What appliances contribute most to overall energy usage?",
      "How can potential savings from energy audits be maximized?",
      "What patterns exist in electric vehicle charging during off-peak hours?"
    ],
    "suggestedQueries": [
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        "title": "Join Smart Meter and Appliance Usage Data",
        "sql": "SELECT sm.timestamp, sm.energy_consumption_kwh, au.appliance_type, au.usage_hours FROM smart_meters sm JOIN appliance_usage au ON sm.home_id = au.home_id WHERE sm.timestamp = au.timestamp",
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      {
        "title": "Aggregate Solar Energy Generation",
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        "dialect": "postgres"
      },
      {
        "title": "Energy Consumption Over Time",
        "sql": "SELECT sm.timestamp, AVG(sm.energy_consumption_kwh) as avg_consumption FROM smart_meters sm GROUP BY sm.timestamp ORDER BY sm.timestamp",
        "dialect": "postgres"
      },
      {
        "title": "Electric Vehicle Charging Patterns",
        "sql": "SELECT ev.home_id, COUNT(ev.id) as charge_count FROM electric_vehicles ev GROUP BY ev.home_id HAVING COUNT(ev.id) > 5",
        "dialect": "postgres"
      },
      {
        "title": "Potential Savings from Energy Audits",
        "sql": "SELECT ha.home_id, SUM(ea.potential_savings) as total_savings FROM energy_audits ea JOIN homes ha ON ea.home_id = ha.id GROUP BY ha.home_id",
        "dialect": "postgres"
      }
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    "usageScenarios": [
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        "title": "Energy Analysts Monitoring Consumption Patterns",
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      },
      {
        "title": "Solar Energy Production Analysis",
        "body": "Researchers studying solar energy production can leverage the solar_installations table to assess energy generation trends relative to weather conditions. By correlating solar generation data with sunny days, they can provide valuable insights into the effectiveness of solar installations and recommend improvements."
      },
      {
        "title": "Electric Vehicle Charging Optimization",
        "body": "Electric vehicle owners and utility companies can benefit from analyzing the electric_vehicles table to optimize charging times. By identifying patterns in charging behavior, they can encourage off-peak charging, reducing strain on the grid and enhancing overall energy efficiency."
      },
      {
        "title": "Identifying Energy Efficiency Opportunities",
        "body": "Home energy auditors can utilize the energy_audits table to identify potential energy savings for clients. By analyzing audit results, they can recommend specific changes that maximize efficiency and reduce energy costs, ultimately guiding homeowners towards a more sustainable lifestyle."
      }
    ]
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