Short-term electrical load forecasting using least squares support vector machines

This paper presents a least squares support vector machines (LS-SVM) approach to short-term electric load forecasting (STLF). The proposed algorithm is more robust and reliable as compared to the traditional approach when actual loads are forecasted and used as input variables. In order to provide t...

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Bibliographic Details
Main Authors: Li Yuancheng, Fang Tingjian, Yu Erkeng
Format: Conference Proceeding
Language:English
Subjects:
Economic forecasting
Equations
Kernel
Least squares methods
Load forecasting
Predictive models
Quadratic programming
Robustness
Space technology
Support vector machines
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Summary:This paper presents a least squares support vector machines (LS-SVM) approach to short-term electric load forecasting (STLF). The proposed algorithm is more robust and reliable as compared to the traditional approach when actual loads are forecasted and used as input variables. In order to provide the forecasted load, the LS-SVM interpolates among the load and temperature data in a training data set. Analysis of the experimental results proved that this approach can achieve greater forecasting accuracy than the traditional model.
DOI:10.1109/ICPST.2002.1053540