Wavelet Transform and Variants of SVR with Application in Wind Forecasting

  • Harsh S. DhimanEmail author
  • Pritam Anand
  • Dipankar Deb
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 757)


Accurate wind prediction plays an important role in grid integration. In this paper, we analyze the performance of a hybrid forecasting method comprising of wavelet transform and different variants of Support Vector Regression (SVR) like \(\varepsilon \)-SVR, Least Square Support Vector Regression (LS-SVR), Twin Support Vector Regression (TSVR) and \(\varepsilon \)-Twin Support Vector Regression (\(\varepsilon \)-TSVR). Each of these methods is trained and tested for a wind farm Sotavento, Galicia, Spain. Wavelet transform is used to filter the raw wind speed data from any kind of stochastic volatility. Among the different variants of SVR, the forecasting results of \(\varepsilon \)-TSVR and TSVR are compared with \(\varepsilon \)-SVR and LS-SVR to evaluate various quantitative measures like RMSE, MAE, SSR/SST and SSE/SST.


Wind forecasting Support Vector Regression (SVR) Least Square Support Vector Regression (LS-SVR) Twin support vector regression (TSVR) \(\varepsilon \)-Twin Support Vector Regression (\(\varepsilon \)-TVSR) 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Institute of Infrastructure Technology Research and ManagementAhmedabadIndia
  2. 2.Department of Computer ScienceSouth Asian UniversityNew DelhiIndia

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