An Integrated Machine Learning Model for Day-Ahead Electricity Price Forecasting

Author

Fan, Shu ; Liao, James R. ; Kaneko, Kazuhiro ; Chen, Luonan

Author_Institution

Osaka Sangyo Univ.

fYear

2006

fDate

Oct. 29 2006-Nov. 1 2006

Firstpage

1643

Lastpage

1649

Abstract

This paper proposes a novel model for short-term electricity price forecasting based on an integration of two machine learning technologies: Bayesian clustering by dynamics (BCD) and support vector machine (SVM). The proposed forecasting system adopts an integrated architecture. Firstly, a BCD classifier is applied to cluster the input data set into several subsets in an unsupervised manner. Then, groups of 24 SVMs for the next day´s electricity price profile are used to fit the training data of each subset in a supervised way. To demonstrate the effectiveness, the proposed model has been trained and tested on the data of the historical energy prices from the New England electricity market

Keywords

Bayes methods; economic forecasting; learning (artificial intelligence); pattern classification; pattern clustering; power markets; power system analysis computing; power system economics; pricing; support vector machines; BCD classifier; Bayesian clustering; New England electricity market; SVM; day-ahead electricity price forecasting; historical energy prices; integrated machine learning model; short-term forecasting; supervised learning; support vector machine; unsupervised learning; Bayesian methods; Economic forecasting; Load forecasting; Machine learning; Predictive models; Support vector machine classification; Support vector machines; Switches; Technology forecasting; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Systems Conference and Exposition, 2006. PSCE '06. 2006 IEEE PES

Conference_Location

Atlanta, GA

Print_ISBN

1-4244-0177-1

Electronic_ISBN

1-4244-0178-X

Type

conf

DOI

10.1109/PSCE.2006.296159

Filename

4075985