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A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems
Energies 2011, 4(6), 960-977; doi:10.3390/en4060960

SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting

1,* , 2
1 Department of Information Management, Oriental Institute of Technology/58 Sec. 2, Sichuan Rd., Panchiao, Taipei 200, Taiwan 2 Department of Organization and Management, Xi’an Jiaotong University, Xi’an 710049, China 3 Department of Global Marketing and Logistics, MingDao University/369 Wen-Hua Rd., Peetow, Changhua 52345, Taiwan 4 Department & Graduate Institute of Finance, National Yunlin University of Science & Technology/123 Sec. 3, University Rd., Douliou, Yunlin 640, Taiwan
* Author to whom correspondence should be addressed.
Received: 24 March 2011 / Revised: 7 June 2011 / Accepted: 14 June 2011 / Published: 17 June 2011
(This article belongs to the Special Issue Intelligent Energy Demand Forecasting)
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Accurate electric load forecasting has become the most important issue in energy management; however, electric load demonstrates a seasonal/cyclic tendency from economic activities or the cyclic nature of climate. The applications of the support vector regression (SVR) model to deal with seasonal/cyclic electric load forecasting have not been widely explored. The purpose of this paper is to present a SVR model which combines the seasonal adjustment mechanism and a chaotic immune algorithm (namely SSVRCIA) to forecast monthly electric loads. Based on the operation procedure of the immune algorithm (IA), if the population diversity of an initial population cannot be maintained under selective pressure, then IA could only seek for the solutions in the narrow space and the solution is far from the global optimum (premature convergence). The proposed chaotic immune algorithm (CIA) based on the chaos optimization algorithm and IA, which diversifies the initial definition domain in stochastic optimization procedures, is used to overcome the premature local optimum issue in determining three parameters of a SVR model. A numerical example from an existing reference is used to elucidate the forecasting performance of the proposed SSVRCIA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the ARIMA and TF-ε-SVR-SA models, and therefore the SSVRCIA model is a promising alternative for electric load forecasting.
Keywords: support vector regression (SVR); seasonal adjustment; chaotic immune algorithm (CIA); electric load forecasting support vector regression (SVR); seasonal adjustment; chaotic immune algorithm (CIA); electric load forecasting
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Hong, W.-C.; Dong, Y.; Lai, C.-Y.; Chen, L.-Y.; Wei, S.-Y. SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting. Energies 2011, 4, 960-977.

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