Load forecasting using support vector machines: A study on EUNITE competition 2001

Chen, BJ

doi:10.1109/TPWRS.2004.835679

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Load forecasting using support vector machines: A study on EUNITE competition 2001

2004-11-01

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Chen, BJ

Ming‐Wei Chang

Chih‐Jen Lin

DOI：10.1109/TPWRS.2004.835679

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Abstract

En 中文

Load forecasting is usually made by constructing models on relative information, such as climate and previous load demand data. In 2001, EUNITE network organized a competition aiming at mid-term load forecasting (predicting daily maximum load of the next 31 days). During the competition we proposed a support vector machine (SVM) model, which was the winning entry, to solve the problem. In this paper, we discuss in detail how SVM, a new learning technique, is successfully applied to load forecasting. In addition, motivated by the competition results and the approaches by other participants, more experiments and deeper analyses are conducted and presented here. Some important conclusions from the results are that temperature (or other types of climate information) might not be useful in such a mid-term load forecasting problem and that the introduction of time-series concept may improve the forecasting.

Keywords:

load forecasting

regression

support vector machines

time series