Multi-Horizon Electricity Demand Forecasting in High Resolution
Add holidays to the mid-term series
Add holidays to the short-term series
Combine forecast models for future predictions
Combine forecast models
Decomposing the load data into long-, mid- and short-term component
Replace missing values in the load data set
Title
Load data from the ENTSO-E Transparency Platform
Load historic yearly average load data
Load a list of macroeconomic data from WDI
Load weather data via API
Get future predictions for the macro economic covariates
Long-term trend predictions for future years
Long-term forecast
Generate future mid-term demand predictions
Mid-term forecast
Generate future short-term demand predictions
Short-term forecast
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.