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Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts

  • Pierre GaillardEmail author
  • Yannig Goude
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 217)

Abstract

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.

Keywords

Generalize Additive Model Time Instance Aggregation Rule Specialized Expert Learning Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank the anonymous reviewers, the editors, and Gilles Stoltz for their valuable comments and feedback.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.EDF R&D, 1 av du Général de GaulleClamartFrance
  2. 2.GREGHEC, CNRSJouy-en-JosasFrance

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