Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach

• 成果类型: Article
• 署名作者: Cho, Haeran; Goude, Yannig; Brossat, Xavier; Yao, Qiwei
• 署名单位: University of London; London School Economics & Political Science; Electricite de France (EDF); Peking University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2012.722900
• 发表日期: 2013
• 页码: 7-21
• 关键词: finite dimensionality; weather; demand; number

摘要

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.