• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 22 Issue 5
Mar.  2010
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Article Contents
CHEN Weirong, ZHENG Yongkang, DAI Chaohua, WANG Weibo. Short-Term Load Forecasting Based on Complex Morlet Wavelet SVM[J]. Journal of Southwest Jiaotong University, 2009, 22(5): 631-636.
Citation: CHEN Weirong, ZHENG Yongkang, DAI Chaohua, WANG Weibo. Short-Term Load Forecasting Based on Complex Morlet Wavelet SVM[J]. Journal of Southwest Jiaotong University, 2009, 22(5): 631-636.

Short-Term Load Forecasting Based on Complex Morlet Wavelet SVM

  • Received Date: 09 Nov 2008
  • Publish Date: 12 Nov 2009
  • In view of the advantage of wavelet analyses in subtle feature extraction and the good global optimization ability of cloud theory-based genetic algorithm (CGA),a CGA-based complex Morlet wavelet SVM (support vector machine),called CGA-CMW-SVM for short,was proposed to improve the forecasting precision and easily select the parameters of SVM.In the CGA-CMW-SVM,the complex Morlet wavelet is used as the kernel function,and the CGA is adopted to optimize the parameters.To decrease the system complexity in short-term load forecasting,the load time series were reconstructed based on the phase space reconstruction theory and their chaotic characteristics only by considering the historical load data without other factors,such as weather and holidays.Though it is believed that the single load data is often characterized as incomplete and inaccurate information,the phase space reconstruction can overcome the shortcomings.Then,the phase space vectors were used as the inputs of the CGA-CMW-SVM for short-term load forecasting.The simulation experiments show that the presented method has small average and maximum errors,and its average errors are less than 1.3400% with a minimum value of 1.0087%.

     

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