{
  "format": "dataset-manifest/1",
  "slug": "renewable-energy-monitor",
  "version": 1,
  "seed": "renewable-energy-monitor:1",
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    "format": "dataset/1",
    "slug": "renewable-energy-monitor",
    "name": "Renewable Energy Insights",
    "tagline": "Events and operations data from renewable energy facilities, including maintenance, performance, and grid curtailments.",
    "description": "Businesses in the renewable energy sector focus on optimizing energy production and usage, often facing challenges like grid curtailments and maintenance of advanced technology such as inverters and batteries. This dataset revolves around events and operations related to energy facilities, providing a detailed record of technician performance, maintenance ticket statuses, and sensor health monitoring various installations. Each entry captures critical moments, such as grid curtailments that affect energy output, alongside more routine reports and maintenance activities. With this data, users can analyze operational trends over time, identifying patterns in performance and maintenance issues that impact energy production. The dataset is synthetic and designed to help analysts and decision-makers understand the dynamics of renewable energy operations.",
    "domain": "energy",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "renewable energy",
      "maintenance",
      "grid curtailment",
      "sensor health",
      "energy production",
      "technician performance"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "energy_operations": {
        "description": "This table captures time-stamped events related to energy facility operations. Each row represents a logged event, detailing the status of various components, technician activities, and any grid curtailments that occurred.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each event."
          },
          "event_timestamp": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date and time when the event was recorded.",
            "dayProfile": "business"
          },
          "event_type": {
            "kind": "enum",
            "values": [
              "maintenance",
              "curtailment",
              "report"
            ],
            "description": "The type of event logged, indicating whether it's maintenance, curtailment, or routine reporting."
          },
          "technician_id": {
            "kind": "uuid",
            "description": "Unique identifier for the technician responsible for the logged event."
          },
          "sensor_health": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "Health status of the sensor monitoring the installation, from 0 to 100 percent."
          },
          "maintenance_status": {
            "kind": "enum",
            "values": [
              "open",
              "in_progress",
              "closed"
            ],
            "nullable": true,
            "description": "The current status of any maintenance ticket associated with the event."
          },
          "output_kwh": {
            "kind": "decimal",
            "min": 0,
            "max": 10000,
            "scale": 2,
            "description": "Energy output in kilowatt-hours during the logged event."
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "timestamped",
      "monetary",
      "categorical"
    ],
    "analysisQuestions": [
      "What are the most common types of events logged over time?",
      "How often do grid curtailments occur, and what is their impact on energy output?",
      "What trends can be observed in technician performance across different events?"
    ],
    "suggestedQueries": [
      {
        "title": "Count Events by Type",
        "sql": "SELECT event_type, COUNT(*) FROM energy_operations GROUP BY event_type;",
        "dialect": "postgres"
      },
      {
        "title": "Average Output During Maintenance",
        "sql": "SELECT AVG(output_kwh) FROM energy_operations WHERE event_type = 'maintenance';",
        "dialect": "postgres"
      },
      {
        "title": "Monthly Grid Curtailments",
        "sql": "SELECT DATE_TRUNC('month', event_timestamp) AS month, COUNT(*) FROM energy_operations WHERE event_type = 'curtailment' GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      },
      {
        "title": "Technician Performance Over Time",
        "sql": "SELECT technician_id, COUNT(*) AS event_count FROM energy_operations GROUP BY technician_id ORDER BY event_count DESC;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Operational Analysis",
        "body": "Energy facility managers can use this dataset to gain insights into the frequency and impact of maintenance activities and grid curtailments. By analyzing the logs, they can improve operational efficiency and technician performance, ultimately enhancing energy production."
      },
      {
        "title": "Performance Reporting",
        "body": "Data analysts in renewable energy companies can leverage this dataset to create reports on key performance indicators (KPIs). By visualizing trends in energy output and maintenance status, they can present valuable information to stakeholders and support decision-making."
      },
      {
        "title": "Predictive Maintenance",
        "body": "Data scientists can apply machine learning techniques to this dataset to predict maintenance needs based on historical trends. By identifying patterns in technician performance and sensor health, they can proactively address potential issues before they lead to downtime."
      }
    ]
  },
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    }
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