This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Wang, J.-M. Articles by Wang, L.-P. Search for Related Content Social Bookmarking                         What's this?

A new method for short-term electricity load forecasting

Department of Economics and Management, North China Electric Power University P.O. Box 327, No. 2, Huadian Rd, 071003 Baoding, Hebei Province, P.R. China

Li-Ping Wang

Department of Economics and Management, North China Electric Power University P.O. Box 327, No. 2, Huadian Rd, 071003 Baoding, Hebei Province, P.R. China, wlp112{at}163.com

Accurate forecasting of short-term electricity load is an important issue in the electricity industry. This paper proposes a new forecasting model by integrating the support vector machines (SVMs) forecasting technique and rough sets (RSs) with reduced attributes using evolutionary algorithms (EAs). Simulation results show that this new model can improve the prediction accuracy, speed the convergence and require less computational effort in comparison with another two methods, namely the traditional SVM model and a model combining the SVMs and simulated annealing algorithms (SVMSA). This improvement is related to fact that the RS techniques can reduce the SVM input variables and improve the convergence.

Key Words: evolutionary algorithm • load forecasting • rough sets • support vector machines.

Transactions of the Institute of Measurement and Control, Vol. 30, No. 3-4, 331-344 (2008)
DOI: 10.1177/0142331208090626


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?