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Support Vector Regression with Chaotic Hybrid Algorithm in Cyclic Electric Load Forecasting

  • Wei-Chiang Hong
  • Yucheng Dong
  • Li-Yueh Chen
  • B. K. Panigrahi
  • Shih-Yung Wei
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 130)

Abstract

Application of support vector regression (SVR) with chaotic sequence and evolutionary algorithms not only could improve forecasting accuracy performance, but also could effectively avoid converging prematurely. However, the tendency of electric load sometimes reveals cyclic changes due to seasonal economic activities or climate seasonal nature. The applications of SVR model to deal with cyclic electric load forecasting have not been widely explored. This investigation presents a SVR-based electric load forecasting model which applied a novel hybrid algorithm, namely chaotic genetic algorithm-simulated annealing algorithm (CGASA), to improve the forecasting performance. In addition, seasonal adjustment mechanism is also employed to deal with cyclic electric loading tendency. A numerical example from an existed reference is used to elucidate the forecasting performance of the proposed seasonal support vector regression with chaotic genetic algorithm, namely SSVRCGASA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models in existed papers. Therefore, the SSVRCGASA model is a promising alternative for electric load forecasting.

Keywords

Support vector regression (SVR) Chaotic genetic algorithm-simulated annealing (CGASA) Seasonal adjustment mechanism Cyclic electric load forecasting 

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Copyright information

© Springer India Pvt. Ltd. 2012

Authors and Affiliations

  • Wei-Chiang Hong
    • 1
  • Yucheng Dong
    • 2
  • Li-Yueh Chen
    • 3
  • B. K. Panigrahi
    • 4
  • Shih-Yung Wei
    • 1
  1. 1.Department of Information ManagementOriental Institute of TechnologyTaipeiTaiwan, R.O.C.
  2. 2.Department of Organization and ManagementXi’an Jiaotong UniversityXi’anChina
  3. 3.Department of Global Marketing and LogisticsMingDao UniversityChanghuaTaiwan, R.O.C.
  4. 4.Department of Electrical EngineeringIndian Institutes of Technology (IITs)DelhiIndia

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