Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach

Haeran Cho*, Yannig Goude, Xavier Brossat, Qiwei Yao

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

40 Citations (Scopus)

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 Electricite de France operational model. Supplementary materials for this article are available online.

Original languageEnglish
Pages (from-to)7-21
Number of pages15
JournalJournal of the American Statistical Association
Volume108
Issue number501
DOIs
Publication statusPublished - 2014

Keywords

  • Generalized additive models
  • Correlation dimension
  • DEMAND
  • NUMBER
  • LONGITUDINAL DATA
  • FUNCTIONAL DATA-ANALYSIS
  • Electricity loads
  • Singular value decomposition
  • WEATHER
  • STOCHASTIC-PROCESSES
  • FINITE DIMENSIONALITY
  • TIME-SERIES
  • Dimension reduction

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