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Support Vector Regression Based on Grid‐Search Method for Short‐Term Wind Power Forecasting

Author

Listed:
  • Hong Zhang
  • Lixing Chen
  • Yong Qu
  • Guo Zhao
  • Zhenwei Guo

Abstract

The purpose of this paper is to investigate the short‐term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid‐search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.

Suggested Citation

  • Hong Zhang & Lixing Chen & Yong Qu & Guo Zhao & Zhenwei Guo, 2014. "Support Vector Regression Based on Grid‐Search Method for Short‐Term Wind Power Forecasting," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:835791
    DOI: 10.1155/2014/835791
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    References listed on IDEAS

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