AI Summary & Key Takeaways
AI InsightRegional holidays, average temperatures, and lag characteristics could improve the model (beyond H-48).
📌 3 Key Takeaways
Abstract
Stability requires energy demand prediction. We train and test 24-hour German load forecasting models. ENTSO-E Transparency Platform data covered European energy generation, transmission, and consumption. It uses German load data instead of PJM data for the eastern US, adds holidays and lag features to the XGB model, and benchmarks with a linear model and a random forest. Grid search CV refines the final XGB model. National load forecasting RMSE is 1740MW, which is suitable for the gradient boosting model. H-24 and H-48 lag is the most important for this job. Weekends and holidays help, but less. Regional holidays, average temperatures, and lag characteristics could improve the model (beyond H-48).