German Electricity Load Data for Probabilistic Demand Forecasting

A methodology paper by Brenda López Cabrera of Humboldt-Universität zu Berlin proposes a functional data approach for forecasting generalized quantiles of electricity demand. The method is applied to load data from a transmission system operator and a balancing unit in Germany and is evaluated against other models, outperforming them in terms of mean absolute percentage error and mean squared error. Supplementary materials for the article are available online.

Use Cases

• Generating probabilistic electricity load forecasts based on the described functional data methodology.
• Evaluating tail event forecasts (quantiles/expectiles) for risk management in energy trading.
• Incorporating explanatory variables like meteorological forecasts into flexible distributional forecast models.
• Benchmarking new forecasting models against the reported mean absolute percentage error and mean squared error performance.

Strengths

• The proposed methodology outperforms other models including the TSO forecast model in terms of mean absolute percentage error and mean squared error.
• The approach allows flexible inclusion of explanatory variables like meteorological forecasts and makes no distributional assumptions.
• The dataset is associated with a published research paper, suggesting a defined methodological context.

Limitations

• Row count, file formats, and column-level documentation are unknown, which limits suitability assessment.
• The actual data files are not directly described; the primary artifact is a methodological paper with supplementary materials.
• Last update date is unknown; freshness unverified.

Provenance

Source

Humboldt-Universität zu Berlin

Collection Method

Likely collected from a transmission system operator (TSO) and a balancing unit in Germany, as mentioned in the methodology application.

Geography

Germany