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  "Title": "Multi-Horizon Electricity Demand Forecasting in High Resolution",
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  "Description": "Advanced forecasting algorithms for long-term energy\ndemand at the national or regional level. The methodology is\nbased on Grandón et al. (2024)\n<doi:10.1016/j.apenergy.2023.122249>; Zimmermann & Ziel (2024)\n<doi:10.1016/j.apenergy.2025.125444>. Real-time data, including\npower demand, weather conditions, and macroeconomic indicators,\nare provided through automated API integration with various\ninstitutions. The modular approach maintains transparency on\nthe various model selection processes and encompasses the\nability to be adapted to individual needs. 'oRaklE' tries to\nhelp facilitating robust decision-making in energy management\nand planning.",
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      "title": "Combine forecast models",
      "topics": [
        "combine_models"
      ]
    },
    {
      "page": "combine_models_future",
      "title": "Combine forecast models for future predictions",
      "topics": [
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      ]
    },
    {
      "page": "decompose_load_data",
      "title": "Decomposing the load data into long-, mid- and short-term component",
      "topics": [
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      ]
    },
    {
      "page": "example_decomposed_data",
      "title": "Example Decomposed Data",
      "topics": [
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      ]
    },
    {
      "page": "example_demand_data",
      "title": "Example Demand Data",
      "topics": [
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    },
    {
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      "title": "Example Demand Data Filled",
      "topics": [
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      ]
    },
    {
      "page": "example_full_model_future_predictions",
      "title": "Example Full Model Future Predictions Data",
      "topics": [
        "example_full_model_future_predictions"
      ]
    },
    {
      "page": "example_full_model_predictions",
      "title": "Example Full Model Predictions Data",
      "topics": [
        "example_full_model_predictions"
      ]
    },
    {
      "page": "example_longterm_and_macro_data",
      "title": "Example Longterm and Macro Data",
      "topics": [
        "example_longterm_and_macro_data"
      ]
    },
    {
      "page": "example_longterm_data",
      "title": "Example Longterm Data",
      "topics": [
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      ]
    },
    {
      "page": "example_longterm_future_macro_data",
      "title": "Example Longterm Future Macro Data",
      "topics": [
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      ]
    },
    {
      "page": "example_longterm_future_predictions",
      "title": "Example Longterm Future Predictions Data",
      "topics": [
        "example_longterm_future_predictions"
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    },
    {
      "page": "example_longterm_predictions",
      "title": "Example Longterm Predictions Data",
      "topics": [
        "example_longterm_predictions"
      ]
    },
    {
      "page": "example_midterm_demand_and_weather_data",
      "title": "Example Midterm Demand and Weather Data",
      "topics": [
        "example_midterm_demand_and_weather_data"
      ]
    },
    {
      "page": "example_midterm_demand_data",
      "title": "Example Midterm Demand Data This dataset contains the seasonal mid-term demand (the difference between the yearly average hourly electricity demand and the daily average hourly electricity demand) for each day for France from 2017 until 2021.",
      "topics": [
        "example_midterm_demand_data"
      ]
    },
    {
      "page": "example_midterm_future_predictions",
      "title": "Example Midterm Future Predictions Data",
      "topics": [
        "example_midterm_future_predictions"
      ]
    },
    {
      "page": "example_midterm_predictions",
      "title": "Example Midterm Predictions Data",
      "topics": [
        "example_midterm_predictions"
      ]
    },
    {
      "page": "example_shortterm_demand_data",
      "title": "Example Short-term Demand Data",
      "topics": [
        "example_shortterm_demand_data"
      ]
    },
    {
      "page": "example_shortterm_future_predictions",
      "title": "Example Shortterm Future Predictions Data",
      "topics": [
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    },
    {
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      "title": "Example Shortterm Predictions Data",
      "topics": [
        "example_shortterm_predictions"
      ]
    },
    {
      "page": "fill_missing_data",
      "title": "Replace missing values in the load data set",
      "topics": [
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    },
    {
      "page": "full_forecast",
      "title": "Title",
      "topics": [
        "full_forecast"
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    },
    {
      "page": "get_entsoE_data",
      "title": "Load data from the ENTSO-E Transparency Platform",
      "topics": [
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    },
    {
      "page": "get_historic_load_data",
      "title": "Load historic yearly average load data",
      "topics": [
        "get_historic_load_data"
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    },
    {
      "page": "get_macro_economic_data",
      "title": "Load a list of macroeconomic data from WDI",
      "topics": [
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      ]
    },
    {
      "page": "get_weather_data",
      "title": "Load weather data via API",
      "topics": [
        "get_weather_data"
      ]
    },
    {
      "page": "long_term_future",
      "title": "Long-term trend predictions for future years",
      "topics": [
        "long_term_future"
      ]
    },
    {
      "page": "long_term_future_data",
      "title": "Get future predictions for the macro economic covariates",
      "topics": [
        "long_term_future_data"
      ]
    },
    {
      "page": "long_term_lm",
      "title": "Long-term forecast",
      "topics": [
        "long_term_lm"
      ]
    },
    {
      "page": "mid_term_future",
      "title": "Generate future mid-term demand predictions",
      "topics": [
        "mid_term_future"
      ]
    },
    {
      "page": "mid_term_lm",
      "title": "Mid-term forecast",
      "topics": [
        "mid_term_lm"
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    {
      "page": "short_term_future",
      "title": "Generate future short-term demand predictions",
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        "short_term_future"
      ]
    },
    {
      "page": "short_term_lm",
      "title": "Short-term forecast",
      "topics": [
        "short_term_lm"
      ]
    },
    {
      "page": "weo_data",
      "title": "World Economic Outlook (WEO) Data",
      "topics": [
        "weo_data"
      ]
    }
  ],
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  "_vignettes": [
    {
      "source": "oRaklE.Rmd",
      "filename": "oRaklE.html",
      "title": "oRaklE: Multi-Horizon Electricity Demand Forecasting in High Resolution",
      "author": "Johannes Schwenzer, Simone Maxand, Tatiana C. G. Grandón",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Package Workflow",
        "Usage",
        "Usage recommendation and things to consider",
        "Step 1: Data Acquisition and Preparation",
        "Retrieve Load Data:",
        "Fill Missing Data:",
        "Decomposition of Load Data",
        "Step 2: Long-Term Trend Modeling",
        "Retrieve Historical Data:",
        "Add Macroeconomic Covariates:",
        "Derive Long-Term Prediction Models:",
        "Generate Future Predictions:",
        "Step 3: Mid-Term Seasonality Modeling",
        "Add Holidays:",
        "Retrieve Weather Data:",
        "Derive Mid-Term Prediction Models:",
        "1. Temperature Transformation Method",
        "2. Spline Method",
        "Step 4: Short-Term Seasonality Modeling",
        "Derive Short-Term Prediction Models:",
        "Step 5: Combine All Models",
        "Generate Future Forecasts:",
        "All-in-One Function"
      ],
      "created": "2025-04-29 08:50:02",
      "modified": "2025-05-05 19:27:36",
      "commits": 2
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