Modeling and forecasting daily electricity load curves: a hybrid approach

Haeran Cho, Yannig Goude, Xavier Brossat and Qiwei Yao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We propose a hybrid approach for the modeling and the short-term forecasting of electricity loads. Two building blocks of our approach are (1) modeling the overall trend and seasonality by fitting a generalized additive model to the weekly averages of the load and (2) modeling the dependence structure across consecutive daily loads via curve linear regression. For the latter, a new methodology is proposed for linear regression with both curve response and curve regressors. The key idea behind the proposed methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several ordinary (i.e., scalar) linear regression problems. We illustrate the hybrid method using French electricity loads between 1996 and 2009, on which we also compare our method with other available models including the Électricité de France operational model. Supplementary materials for this article are available online.

Keywords: correlation dimension; dimension reduction; electricity loads; generalized additive models; singular value decomposition (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (24)

Published in Journal of the American Statistical Association, 2013, 108(501), pp. 7-21. ISSN: 0162-1459

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:49634

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