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Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, 400030, China
School of Automation Engineering, Chongqing University, 400030, China
Chongqing Tongnan Electric Power Company, Chongqing, 402660, China
* Author to whom correspondence should be addressed.
Received: 14 November 2010; in revised form: 13 December 2010 / Accepted: 5 January 2011 / Published: 20 January 2011
Abstract: This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique according to the degree of similarity of the input samples considering the periodic characteristics of the load. PSO is applied to optimize the model parameters. The complicated nonlinear relationships between the factors influencing the load and the load forecasting can be regressed using the SVR. The practical load data from a city in Chongqing was used to illustrate the proposed method, and the results indicate that the proposed method can obtain higher accuracy compared with the traditional method, and is effective for forecasting the short-term load of power systems.
Keywords: load forecasting; short-time load; PSO
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Duan, P.; Xie, K.; Guo, T.; Huang, X. Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques. Energies 2011, 4, 173-184.
Duan P, Xie K, Guo T, Huang X. Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques. Energies. 2011; 4(1):173-184.
Duan, Pan; Xie, Kaigui; Guo, Tingting; Huang, Xiaogang. 2011. "Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques." Energies 4, no. 1: 173-184.