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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aiolfi, M., Capistrán, C., & Timmermann, A. (2010). Forecast combinations (Working Papers 2010-04). Banco de México. http://EconPapers.repec.org/RePEc:bdm:wpaper:2010-04.
Antoniadis, A., Brossat, X., Cugliari, J., & Poggi, J. (2012). Prévision d’un processus à valeurs fonctionnelles en présence de non stationnarités. Application à la consommation d’électricité. Journal de la Société Franaise de Statistique, 153(2), 52–78.
Antoniadis, A., Brossat, X., Cugliari, J., & Poggi, J. (2013). Clustering functional data using wavelets. International Journal of Wavelets, Multiresolution and Information Processing, 11(1), 1–30.
Antoniadis, A., Paparoditis, E., & Sapatinas, T. (2006). A functional wavelet–kernel approach for time series prediction. Journal of the Royal Statistical Society: Series B, 68(5), 837–857.
Azoury, K. S., & Warmuth, M. K. (2001). Relative loss bounds for on-line density estimation with the exponential family of distributions. Machine Learning, 43(3), 211–246.
Biau, G., & Patra, B. (2011). Sequential quantile prediction of time series. IEEE Transactions on Information Theory, 57(3), 1664–1674.
Breiman, L. (1996). Bagging predictor. Machine Learning, 24(2), 123–140.
Cesa-Bianchi, N., & Lugosi, G. (2003). Potential-based algorithms in on-line prediction and game theory. Machine Learning, 51(3), 239–261.
Cesa-Bianchi, N., & Lugosi, G. (2006). Prediction, learning, and games. Cambridge/ New York: Cambridge University Press.
Cho, H., Goude, Y., Brossat, X., & Yao, Q. (2013). Modeling and forecasting daily electricity load curves: A hybrid approach. Journal of the American Statistical Association, 108, 7–21.
Cho, H., Goude, Y., Brossat, X., & Yao, Q. (2014, to appear). Modeling and forecasting daily electricity load using curve linear regression. In Lecture notes in statistics 217: Modeling and stochastic learning for forecasting in high dimension, 35–52.
Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559–583.
Devaine, M., Gaillard, P., Goude, Y., & Stoltz, G. (2013). Forecasting electricity consumption by aggregating specialized experts. Machine Learning, 90(2), 231–260.
Eban, E., Birnbaum, A., Shalev-Shwartz, S., & Globerson, A. (2012). Learning the experts for online sequence prediction. In Proceedings of ICML, Edinburgh.
Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an applicationto boosting. Journal of Computer and System Sciences, 55, 119–139.
Gaillard, P., Goude, Y., & Stoltz, G. (2011). A further look at the forecasting of the electricity consumption by aggregation of specialized experts (Technical report). pierre.gaillard.me/doc/GaGoSt-report.pdf.
Gaillard, P., Stoltz, G., & van Erven, T. (2014). A second-order bound with excess losses. ArXiv:1402.2044.
Herbster, M., & Warmuth, M. K. (1998). Tracking the best expert. Machine Learning, 32(2), 151–178.
Hoerl, A., & Kennard, R. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 55–67.
Littlestone, N., & Warmuth, M. K. (1994). The weighted majority algorithm. Information and Computation, 108(2), 212–261.
Mallet, V. (2010). Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation. Journal of Geophysical Research, 115(D24303), 1–10.
Mallet, V., Stoltz, G., & Mauricette, B. (2009). Ozone ensemble forecast with machine learning algorithms. Journal of Geophysical Research, 114(D05307), 1–13.
Monteleoni, C., Schmidt, G. A., Saroha, S., & Asplund, E. (2011). Tracking climate models. Statistical Analysis and Data Mining, 4(4), 372–392.
Nedellec, R., Cugliari, J., & Goude, Y. (2014). Gefcom2012: Electric load forecasting and backcasting with semi-parametric models. International Journal of Forecasting, 30(2), 375–381.
Pierrot, A., & Goude, Y. (2011). Short-term electricity load forecasting with generalized additive models. In: Proceedings of ISAP power, Hersonisos, Greece (pp. 593–600).
Vovk, V. (2001). Competitive on-line statistics. International Statistical Review, 69(2), 213–248.
Vovk, V. G. (1990). Aggregating strategies. In Proceedings of the Third Workshop on Computational Learning Theory, Rochester (pp. 371–386).
Wood, S. (2006). Generalized additive models: An introduction with R. Boca Raton: Chapman and Hall/CRC.
Wood, S., Goude, Y., & Shaw, S. (2015). Generalized additive models for large datasets. Journal of Royal Statistical Society, Series C, 64(1), 139–155.
Acknowledgements
We thank the anonymous reviewers, the editors, and Gilles Stoltz for their valuable comments and feedback.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Gaillard, P., Goude, Y. (2015). Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts. In: Antoniadis, A., Poggi, JM., Brossat, X. (eds) Modeling and Stochastic Learning for Forecasting in High Dimensions. Lecture Notes in Statistics(), vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-18732-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-18732-7_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18731-0
Online ISBN: 978-3-319-18732-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)