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Machine learning based switching model for electricity load forecasting
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DOI：10.1016/j.enconman.2008.01.008.png)
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Abstract
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In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords:
electricity load forecasting
machine learning
Bayesian clustering
support vector regression
non-stationarity
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