Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts
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Short-term electricity forecasting has been studied for years at EDF and different forecasting models were developed from various fields of statistics or machine learning (functional data analysis, time series, non-parametric regression, boosting, bagging). We are interested in the forecasting of France’s daily electricity load consumption based on these different approaches. We investigate in this empirical study how to use them to improve prediction accuracy. First, we show how combining members of the original set of forecasts can lead to a significant improvement. Second, we explore how to build various and heterogeneous forecasts from these models and analyze how we can aggregate them to get even better predictions.
KeywordsGeneralize Additive Model Time Instance Aggregation Rule Specialized Expert Learning Parameter
We thank the anonymous reviewers, the editors, and Gilles Stoltz for their valuable comments and feedback.
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