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Energies 2011, 4(1), 173-184; doi:10.3390/en4010173
Article

Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques

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Received: 14 November 2010; in revised form: 13 December 2010 / Accepted: 5 January 2011 / Published: 20 January 2011
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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 load forecasting; short-time load; PSO
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

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.

AMA Style

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.

Chicago/Turabian Style

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.


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