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

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Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 130))

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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.

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Authors and Affiliations

  1. Department of Information Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Rd., Panchiao, Taipei, 220, Taiwan, R.O.C.

    Wei-Chiang Hong & Shih-Yung Wei

  2. Department of Organization and Management, Xi’an Jiaotong University, Xi’an, 710049, China

    Yucheng Dong

  3. Department of Global Marketing and Logistics, MingDao University, 369 Wen-Hua Rd., Peetow, Changhua, 52345, Taiwan, R.O.C.

    Li-Yueh Chen

  4. Department of Electrical Engineering, Indian Institutes of Technology (IITs), Delhi, India

    B. K. Panigrahi

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  1. Wei-Chiang Hong
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  2. Yucheng Dong
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  3. Li-Yueh Chen
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  4. B. K. Panigrahi
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  5. Shih-Yung Wei
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Correspondence to Wei-Chiang Hong .

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Editors and Affiliations

  1. , Department of Mathematics, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Kusum Deep

  2. Department of Mathematics and, Computer Science, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, United Kingdom

    Atulya Nagar

  3. , Department of Paper Technology, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Millie Pant

  4. Information Technology and Management, ABV-Indian Institute of, 109, E- Block, Gwalior, 474010, India

    Jagdish Chand Bansal

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Hong, WC., Dong, Y., Chen, LY., Panigrahi, B.K., Wei, SY. (2012). Support Vector Regression with Chaotic Hybrid Algorithm in Cyclic Electric Load Forecasting. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 130. Springer, India. https://doi.org/10.1007/978-81-322-0487-9_79

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  • DOI: https://doi.org/10.1007/978-81-322-0487-9_79

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