{
  "format": "dataset-manifest/1",
  "slug": "energy-model-optimization",
  "version": 2,
  "seed": "energy-model-optimization:1",
  "spec": {
    "format": "dataset/1",
    "slug": "energy-model-optimization",
    "name": "Energy Model Optimization",
    "tagline": "Optimization runs of machine learning models for energy consumption prediction and grid stability.",
    "description": "Organizations in the energy sector optimize machine learning model performance to enhance grid stability and predict consumption patterns effectively. Teams allocate computational resources, evaluate performance metrics, and fine-tune hyperparameters, all aimed at deriving the best outcomes from various neural network architectures. This operational focus leads to a rich dataset reflecting a variety of optimization runs, mostly clustered around popular environments and well-regarded optimizer choices. Occasionally, rare entries surface, showcasing unique architectures or less commonly tested hyperparameter combinations, adding depth to the analysis.\n\nThe primary table in this dataset, model_optimization, contains detailed records of each optimization attempt, with rows representing individual runs. Each entry includes information about the model architecture used, the specific environment, hyperparameters tested, and performance metrics achieved. This structured approach allows developers to understand which combinations yield optimal results and how different parameters interact within the model training process.\n\nPractitioners can answer questions revolving around the effectiveness of various neural network configurations, the impact of specific hyperparameter choices on performance, and trends in optimization strategies over time. This dataset is a valuable resource for anyone looking to practice data analysis and machine learning optimization techniques, providing insights into the intricate dynamics of model training in the energy sector. Please note that this data is synthetic, designed for educational purposes.",
    "domain": "energy",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "machine learning",
      "energy consumption",
      "model optimization",
      "hyperparameters",
      "neural networks",
      "performance metrics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "model_optimization": {
        "description": "Each row represents a distinct optimization run for a machine learning model, detailing its parameters, performance metrics, and the computational environment used. The table links various hyperparameter choices to their impact on model performance.",
        "rows": 1000,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the optimization run."
          },
          "model_architecture": {
            "kind": "enum",
            "values": [
              "CNN",
              "RNN",
              "LSTM",
              "GRU",
              "Transformer"
            ],
            "description": "The architecture of the neural network used for the optimization."
          },
          "optimizer": {
            "kind": "enum",
            "values": [
              "SGD",
              "Adam",
              "RMSprop",
              "Adagrad",
              "Adadelta"
            ],
            "description": "The optimization algorithm used during training."
          },
          "learning_rate": {
            "kind": "decimal",
            "min": 0.0001,
            "max": 0.1,
            "scale": 4,
            "description": "The learning rate for the optimization algorithm."
          },
          "batch_size": {
            "kind": "int",
            "min": 16,
            "max": 256,
            "description": "The number of samples processed before the model is updated."
          },
          "epochs": {
            "kind": "int",
            "min": 1,
            "max": 100,
            "description": "The number of complete passes through the training dataset."
          },
          "training_time": {
            "kind": "decimal",
            "min": 0.1,
            "max": 120,
            "scale": 2,
            "description": "The time taken for training the model in minutes.",
            "distribution": "long-tail"
          },
          "accuracy": {
            "kind": "decimal",
            "min": 0,
            "max": 1,
            "scale": 4,
            "description": "The accuracy of the model on the validation dataset.",
            "distribution": "high"
          },
          "created_at": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "The timestamp when the optimization run was conducted.",
            "dayProfile": "business"
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "monetary",
      "high-cardinality",
      "nullable"
    ],
    "analysisQuestions": [
      "What optimizer combinations yield the highest accuracy for different model architectures?",
      "How does the learning rate affect training time and model performance?",
      "Which batch sizes are most commonly used, and how do they impact the training outcomes?",
      "What is the average training time for models across different architectures?",
      "Are there any rare hyperparameter combinations that result in significantly better performance?"
    ],
    "suggestedQueries": [
      {
        "title": "Retrieve all optimization runs with high accuracy",
        "sql": "SELECT * FROM model_optimization WHERE accuracy > 0.9;",
        "dialect": "postgres"
      },
      {
        "title": "Count the number of runs for each model architecture",
        "sql": "SELECT model_architecture, COUNT(*) FROM model_optimization GROUP BY model_architecture;",
        "dialect": "postgres"
      },
      {
        "title": "Find the average training time per optimizer",
        "sql": "SELECT optimizer, AVG(training_time) FROM model_optimization GROUP BY optimizer;",
        "dialect": "postgres"
      },
      {
        "title": "List optimization runs sorted by accuracy and training time",
        "sql": "SELECT * FROM model_optimization ORDER BY accuracy DESC, training_time ASC;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Data Science Education",
        "body": "This dataset is ideal for data science educators who want to teach students about model optimization techniques in machine learning. By using realistic synthetic data, students can practice SQL queries and ORM interactions without the complications of real-world data."
      },
      {
        "title": "Research in Energy Consumption",
        "body": "Researchers focusing on energy consumption patterns can utilize this dataset to analyze different modeling techniques and their effectiveness. The variety of hyperparameters allows them to simulate different scenarios and derive insights on optimizing energy predictions."
      },
      {
        "title": "Software Development and Testing",
        "body": "Developers creating applications in the energy sector can use this dataset to seed their databases during development. It helps them test their applications with a comprehensive set of optimization scenarios, ensuring their systems can handle various configurations."
      }
    ]
  },
  "counts": {
    "model_optimization": 1000
  },
  "stats": {
    "model_optimization": {
      "rows": 1000,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 1000,
          "min": 1,
          "max": 1000
        },
        "model_architecture": {
          "nulls": 0,
          "distinct": 5,
          "min": "CNN",
          "max": "Transformer"
        },
        "optimizer": {
          "nulls": 0,
          "distinct": 5,
          "min": "Adadelta",
          "max": "SGD"
        },
        "learning_rate": {
          "nulls": 0,
          "distinct": 638,
          "min": 0.0002,
          "max": 0.1
        },
        "batch_size": {
          "nulls": 0,
          "distinct": 238,
          "min": 16,
          "max": 256
        },
        "epochs": {
          "nulls": 0,
          "distinct": 100,
          "min": 1,
          "max": 100
        },
        "training_time": {
          "nulls": 0,
          "distinct": 913,
          "min": 0.1,
          "max": 119.39
        },
        "accuracy": {
          "nulls": 0,
          "distinct": 917,
          "min": 0.0017,
          "max": 1
        },
        "created_at": {
          "nulls": 0,
          "distinct": 1000,
          "min": "2022-01-01 16:50:29",
          "max": "2023-12-31 19:35:32"
        }
      }
    }
  },
  "artifacts": [
    {
      "file": "csv/model_optimization.csv",
      "format": "csv",
      "table": "model_optimization",
      "key": "datasets/energy-model-optimization/v2/csv/model_optimization.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-model-optimization/v2/csv/model_optimization.csv",
      "bytes": 64040,
      "sha256": "c6950d5d84a233969dd71afc716dd2ccb791aed6295cc923ffb806cca8a155c1",
      "rows": 1000,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "energy-model-optimization.json",
      "format": "json",
      "table": null,
      "key": "datasets/energy-model-optimization/v2/energy-model-optimization.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-model-optimization/v2/energy-model-optimization.json",
      "bytes": 268621,
      "sha256": "a9d5d62aee1cc746183e74617cc85e04f055eae5124f6d15f5f894a1fa07b706",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    },
    {
      "file": "sql/postgres.sql",
      "format": "sql-postgres",
      "table": null,
      "key": "datasets/energy-model-optimization/v2/sql/postgres.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-model-optimization/v2/sql/postgres.sql",
      "bytes": 85601,
      "sha256": "f0b090eff687e75bfe4f0d594ea7e73b5272d967b50ccc089d9be7cbf474d368",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mysql.sql",
      "format": "sql-mysql",
      "table": null,
      "key": "datasets/energy-model-optimization/v2/sql/mysql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-model-optimization/v2/sql/mysql.sql",
      "bytes": 85473,
      "sha256": "01d40de60603b5d90730d73a380912a9f29f57e41b90816a99eb0b2be27c0585",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mssql.sql",
      "format": "sql-mssql",
      "table": null,
      "key": "datasets/energy-model-optimization/v2/sql/mssql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-model-optimization/v2/sql/mssql.sql",
      "bytes": 88557,
      "sha256": "d352540a74299c5bea3ccb45dc84c4c86d718a5305ad687c6edfdc6ea67d2e7d",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/sqlite.sql",
      "format": "sql-sqlite",
      "table": null,
      "key": "datasets/energy-model-optimization/v2/sql/sqlite.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-model-optimization/v2/sql/sqlite.sql",
      "bytes": 85447,
      "sha256": "e4d03d438f17da8bb4372f840f532a71f259500b6f1e4ff14387eaebeecc9449",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "prisma/seed.ts",
      "format": "prisma-seed",
      "table": null,
      "key": "datasets/energy-model-optimization/v2/prisma/seed.ts",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-model-optimization/v2/prisma/seed.ts",
      "bytes": 218787,
      "sha256": "cabf5889263fbfb85545ce94c53a360981277940c35389bada0f2a4c9af775a8",
      "rows": null,
      "contentType": "application/typescript; charset=utf-8"
    },
    {
      "file": "django/fixtures.json",
      "format": "django-fixture",
      "table": null,
      "key": "datasets/energy-model-optimization/v2/django/fixtures.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-model-optimization/v2/django/fixtures.json",
      "bytes": 329944,
      "sha256": "f71bd170f80c1fe49bed4bf00d648f01f2525c8a0415d7b4d0df206d247fb7da",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    }
  ],
  "publishedAt": 1788765872676,
  "updatedAt": 1788778568643
}