example_full_model_future_predictions

This dataset extends the full model predictions from <code>example_full_model_predictions</code> until the year 2028.

dataset

Future

final-predictions

Advanced forecasting algorithms for long-term energy demand at the
national or regional level. The methodology is based on Grandón et al. (2024)
<doi:10.1016/j.apenergy.2023.122249>; Zimmermann & Ziel (2024)
<doi:10.1016/j.apenergy.2025.125444>. Real-time data, including power demand, weather conditions, and macroeconomic indicators, are provided through automated API integration with various institutions. The modular approach maintains transparency on the various model selection processes and encompasses the ability to be adapted to individual needs. 'oRaklE' tries to help facilitating robust decision-making in energy management and planning.

Johannes Schwenzer

oRaklE

Multi-Horizon Electricity Demand Forecasting in High Resolution

Simone Maxand

Tatiana Gonzalez Grandón

example_full_model_future_predictions function

A data frame with 43800 rows and 12 columns:<dl>
<dt><code>country</code></dt>
<dd>The country, represented by the ISO2C country code (e.g., <code>FR</code> for France).</dd><dt><code>date</code></dt>
<dd>The date (in <code>YYYY-MM-DD</code> format).</dd><dt><code>year</code></dt>
<dd>The respective year.</dd><dt><code>month</code></dt>
<dd>The respective month.</dd><dt><code>day</code></dt>
<dd>The respective day.</dd><dt><code>wday</code></dt>
<dd>The type of weekday (e.g., <code>Sun</code>, <code>Mon</code>)</dd><dt><code>hour</code></dt>
<dd>The respective hour of the day.</dd><dt><code>hourly_demand</code></dt>
<dd>The actual hourly electricity demand (in megawatts).</dd><dt><code>long_term_model</code></dt>
<dd>The predicted long-term trend (yearly average of hourly demand) from the best long-term forecasting model.</dd><dt><code>mid_term_model</code></dt>
<dd>The predicted mid-term seasonality (daily minus yearly average of hourly demand) from the best mid-term forecasting model.</dd><dt><code>short_term_model</code></dt>
<dd>The predicted short-term seasonality (actual hourly demand minus the long-term trend minus the mid-term seasonality) from the best short-term forecasting model.</dd><dt><code>complete_model</code></dt>
<dd>Final predicted electricity demand for each hour. Derived by adding the results from the long-, mid-, and short-term components.</dd>
</dl>

Format

Example Full Model Future Predictions Data — example_full_model_future_predictions

A data frame with 43800 rows and 12 columns:<dl>
<dt><code>country</code></dt>
<dd>The country, represented by the ISO2C country code (e.g., <code>FR</code> for France).</dd>

<dt><code>date</code></dt>
<dd>The date (in <code>YYYY-MM-DD</code> format).</dd>

<dt><code>year</code></dt>
<dd>The respective year.</dd>

<dt><code>month</code></dt>
<dd>The respective month.</dd>

<dt><code>day</code></dt>
<dd>The respective day.</dd>

<dt><code>wday</code></dt>
<dd>The type of weekday (e.g., <code>Sun</code>, <code>Mon</code>)</dd>

<dt><code>hour</code></dt>
<dd>The respective hour of the day.</dd>

<dt><code>hourly_demand</code></dt>
<dd>The actual hourly electricity demand (in megawatts).</dd>

<dt><code>long_term_model</code></dt>
<dd>The predicted long-term trend (yearly average of hourly demand) from the best long-term forecasting model.</dd>

<dt><code>mid_term_model</code></dt>
<dd>The predicted mid-term seasonality (daily minus yearly average of hourly demand) from the best mid-term forecasting model.</dd>

<dt><code>short_term_model</code></dt>
<dd>The predicted short-term seasonality (actual hourly demand minus the long-term trend minus the mid-term seasonality) from the best short-term forecasting model.</dd>

<dt><code>complete_model</code></dt>
<dd>Final predicted electricity demand for each hour. Derived by adding the results from the long-, mid-, and short-term components.</dd>


</dl>

example_full_model_future_predictions: Example Full Model Future Predictions Data

Description

Usage

Arguments

Format