{
  "format": "dataset-manifest/1",
  "slug": "augmented-reality-home-energy-efficiency-advisor",
  "version": 2,
  "seed": "augmented-reality-home-energy-efficiency-advisor:1",
  "spec": {
    "format": "dataset/1",
    "slug": "augmented-reality-home-energy-efficiency-advisor",
    "name": "Energy Efficiency Insights",
    "tagline": "Thermal images, energy consumption data, insulation effectiveness, weather factors, and user feedback from homeowners.",
    "description": "Homeowners analyze their properties using augmented reality tools to enhance energy efficiency. These tools provide insights into thermal images, revealing where heat is lost or gained, and track patterns of energy consumption by appliances. The operation focuses on identifying insulation effectiveness in homes, understanding how varying weather conditions impact energy use, and gathering user feedback on suggested improvements. This dynamic environment sees homeowners equipped with the knowledge and technology to make informed changes to reduce their energy footprint. \n\nIn this dataset, the tables relate closely to one another to form a comprehensive picture of energy efficiency. A single row in the thermal_images table represents a thermal image of a specific home, highlighting areas of heat loss. The energy_consumption table captures detailed records of energy usage from various appliances, linked to specific households. The insulation_effectiveness table evaluates how well insulation mitigates energy waste, while weather_conditions provide context for external factors influencing energy usage, such as temperature fluctuations or humidity levels. Lastly, user_feedback offers insights into how homeowners perceive the effectiveness of recommended improvements, tying together the entire operation with qualitative data. \n\nQuestions that can be explored with this data include: What are the common patterns in energy consumption among homes with similar insulation? How do weather conditions correlate with energy spikes in different households? What specific recommendations yielded positive feedback from users and resulted in actual improvements in energy efficiency? Practicing with this synthetic data allows developers to hone their skills in data analysis, predictive modeling, and user experience optimization, addressing the complex interplay of technology and energy management.",
    "domain": "energy",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "energy efficiency",
      "augmented reality",
      "thermal images",
      "energy consumption",
      "insulation",
      "weather",
      "user feedback"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "thermal_images": {
        "description": "This table contains thermal images captured for various homes, linking to specific energy assessments. Each image is associated with a timestamp indicating when it was taken.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each thermal image."
          },
          "property_id": {
            "kind": "uuid",
            "description": "Identifier for the property associated with the thermal image."
          },
          "image_data": {
            "kind": "json",
            "template": {
              "url": {
                "kind": "url",
                "description": "Link to the thermal image."
              },
              "analysis": {
                "kind": "sentence",
                "description": "Short analysis of the thermal image."
              }
            },
            "description": "Data containing the image URL and analysis.",
            "nullable": true
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date and time when the thermal image was captured."
          }
        }
      },
      "energy_consumption": {
        "description": "This table records energy consumption metrics for appliances within homes, with timestamps to indicate the periods of measurement.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each energy consumption record."
          },
          "property_id": {
            "kind": "uuid",
            "description": "Identifier for the property associated with the energy consumption."
          },
          "appliance": {
            "kind": "words",
            "max": 3,
            "description": "Name of the appliance consuming energy.",
            "nullable": true,
            "pool": [
              "refrigerator",
              "washer",
              "dryer",
              "oven",
              "microwave",
              "dishwasher",
              "airconditioner",
              "heater",
              "fan",
              "television",
              "computer",
              "laptop",
              "printer",
              "stove",
              "kettle",
              "toaster",
              "blender",
              "coffee",
              "humidifier",
              "dehumidifier",
              "waterheater",
              "vacuum",
              "iron",
              "electric",
              "range",
              "freezer",
              "stereo",
              "projector",
              "monitor",
              "router",
              "gameconsole",
              "speaker",
              "lightbulb",
              "charger",
              "tablet",
              "smartphone",
              "electricity",
              "cooker",
              "induction",
              "electricfan",
              "electricgrill",
              "electricstove",
              "electrickettle",
              "electricblanket",
              "electricbicycle",
              "electricvehicle",
              "electricmower",
              "electricfireplace"
            ],
            "min": 1
          },
          "consumption_kwh": {
            "kind": "decimal",
            "min": 0,
            "max": 1000,
            "scale": 2,
            "description": "Amount of energy consumed in kilowatt-hours.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date and time of the energy consumption measurement.",
            "dayProfile": "waking"
          }
        }
      },
      "insulation_effectiveness": {
        "description": "This table captures the effectiveness of insulation in homes, measuring heat retention or loss over time.",
        "rows": 250,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each insulation effectiveness record."
          },
          "property_id": {
            "kind": "uuid",
            "description": "Identifier for the property being evaluated."
          },
          "effectiveness_score": {
            "kind": "decimal",
            "min": 0,
            "max": 1,
            "scale": 2,
            "description": "Score indicating the effectiveness of insulation (0 to 1).",
            "nullable": true,
            "distribution": "high"
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date and time of the insulation evaluation.",
            "dayProfile": "business"
          }
        }
      },
      "weather_conditions": {
        "description": "This table provides weather data relevant to energy usage, including temperature and humidity levels.",
        "rows": 400,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each weather record."
          },
          "property_id": {
            "kind": "uuid",
            "description": "Identifier for the property linked to the weather conditions."
          },
          "temperature_c": {
            "kind": "decimal",
            "min": -30,
            "max": 50,
            "scale": 1,
            "description": "Outdoor temperature in degrees Celsius.",
            "nullable": true
          },
          "humidity_percentage": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 1,
            "description": "Outdoor humidity level in percentage.",
            "nullable": true,
            "distribution": "normal"
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date and time of the weather measurement."
          }
        }
      },
      "user_feedback": {
        "description": "This table collects user feedback on suggested energy efficiency improvements, linking to the corresponding property.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each feedback entry."
          },
          "property_id": {
            "kind": "uuid",
            "description": "Identifier for the property associated with the feedback."
          },
          "feedback_text": {
            "kind": "paragraph",
            "max": 500,
            "description": "User's feedback on energy efficiency recommendations.",
            "nullable": true
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date and time when the feedback was provided.",
            "trend": "growing"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "nullable",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "How does insulation effectiveness vary across different properties?",
      "What is the relationship between outdoor temperature and energy consumption for appliances?",
      "Which appliances are most frequently reported for energy spikes?",
      "How do user feedback trends correlate with energy efficiency improvements?",
      "What weather conditions lead to the highest energy consumption?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Thermal Images and User Feedback",
        "sql": "SELECT ti.timestamp, ti.image_data->>'url' AS image_url, uf.feedback_text FROM thermal_images ti JOIN user_feedback uf ON ti.property_id = uf.property_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Energy Consumption by Appliance",
        "sql": "SELECT appliance, AVG(consumption_kwh) AS avg_consumption FROM energy_consumption GROUP BY appliance;",
        "dialect": "postgres"
      },
      {
        "title": "Correlate Weather Conditions with Energy Consumption",
        "sql": "SELECT wc.timestamp, wc.temperature_c, ec.consumption_kwh FROM weather_conditions wc JOIN energy_consumption ec ON wc.property_id = ec.property_id WHERE wc.timestamp = ec.timestamp;",
        "dialect": "postgres"
      },
      {
        "title": "Count User Feedback Entries Over Time",
        "sql": "SELECT date_trunc('month', timestamp) AS month, COUNT(*) AS feedback_count FROM user_feedback GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Energy Auditors Utilizing the Dataset",
        "body": "Energy auditors can leverage this dataset to analyze thermal images and assess the effectiveness of insulation in various homes. By comparing energy consumption patterns with insulation scores, they can provide tailored recommendations to homeowners looking to improve their energy efficiency."
      },
      {
        "title": "Data Analysts Building BI Dashboards",
        "body": "Data analysts can use this dataset to create visualizations that track energy consumption trends over time, correlating them with weather conditions. BI dashboards can be built to provide actionable insights into how different factors impact overall energy usage, aiding in decision-making for energy-saving strategies."
      },
      {
        "title": "Researchers Studying Energy Efficiency",
        "body": "Researchers can utilize this dataset to conduct studies on the effectiveness of various energy efficiency measures. By analyzing user feedback alongside thermal images and weather data, they can gain insights into the most effective strategies for improving energy efficiency in homes."
      },
      {
        "title": "Homeowners Seeking Improvements",
        "body": "Homeowners interested in enhancing their energy efficiency can explore this dataset to understand how their properties compare with others. By analyzing energy consumption data and user feedback, they can identify specific areas for improvement and prioritize their investments in energy-efficient upgrades."
      }
    ]
  },
  "counts": {
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    "insulation_effectiveness": 250,
    "thermal_images": 200,
    "user_feedback": 150,
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