Title :
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.
fDate :
Oct. 29 2006-Nov. 1 2006
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;
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
DOI :
10.1109/PSCE.2006.296159
