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

Author

Listed:
  • Wang, Jianjun
  • Li, Li
  • Niu, Dongxiao
  • Tan, Zhongfu
Abstract
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.

Suggested Citation

  • Wang, Jianjun & Li, Li & Niu, Dongxiao & Tan, Zhongfu, 2012. "An annual load forecasting model based on support vector regression with differential evolution algorithm," Applied Energy, Elsevier, vol. 94(C), pages 65-70.
    • Handle: RePEc:eee:appene:v:94:y:2012:i:c:p:65-70
    DOI: 10.1016/j.apenergy.2012.01.010
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    References listed on IDEAS

    as
    1. Goia, Aldo & May, Caterina & Fusai, Gianluca, 2010. "Functional clustering and linear regression for peak load forecasting," International Journal of Forecasting, Elsevier, vol. 26(4), pages 700-711, October.
    2. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
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