An annual load forecasting model based on support vector regression with differential evolution algorithm

Annual load forecasting is very important for the electric power industry. As influenced by various factors, an annual load curve shows a non-linear characteristic, which demonstrates that the annual load forecasting is a non-linear problem. Support vector regression (SVR) is proven to be useful in...

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Bibliographic Details
Published in:Applied energy Vol. 94; pp. 65 - 70
Main Authors: Wang, Jianjun, Li, Li, Niu, Dongxiao, Tan, Zhongfu
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.06.2012
Elsevier
Subjects:
algorithms
Annual load forecasting
Applied sciences
Differential evolution (DE)
electric power
Energy
Exact sciences and technology
industry
neural networks
Support vector regression (SVR)
Annual load forecasting
Support vector regression (SVR)
Differential evolution (DE)
ISSN:0306-2619, 1872-9118
Online Access:Get full text
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Summary:Annual load forecasting is very important for the electric power industry. As influenced by various factors, an annual load curve shows a non-linear characteristic, which demonstrates that the annual load forecasting is a non-linear problem. Support vector regression (SVR) is proven to be useful in dealing with non-linear forecasting problems in recent years. The key point in using SVR for forecasting is how to determine the appropriate parameters. This paper proposes a hybrid load forecasting model combining differential evolution (DE) algorithm and support vector regression to deal with this problem, where the DE algorithm is used to choose the appropriate parameters for the SVR load forecasting model. The effectiveness of this model has been proved by the final simulation which shows that the proposed model outperforms the SVR model with default parameters, back propagation artificial neural network (BPNN) and regression forecasting models in the annual load forecasting.
Bibliography:http://dx.doi.org/10.1016/j.apenergy.2012.01.010
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2012.01.010