Load forecasting using support vector machines: A study on EUNITE competition 2001
Resource
IEEE Transactions on Power Systems 19 (4): 1821-1830
Journal
IEEE Transactions on Power Systems
Journal Volume
19
Journal Issue
4
Pages
1821-1830
Date Issued
2004
Date
2004
Author(s)
Abstract
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. © 2004 IEEE.
Subjects
Load forecasting; Regression; Support vector machines; Time series
SDGs
Other Subjects
Learning systems; Mathematical models; Regression analysis; Time series analysis; Daily load demand; Mid term load forecasting; Support vector machines; Electric load forecasting
Type
journal article
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