Application of SVM-RBF to Prediction of Short-Term Load

Abstract:

Short Term Load Forecasting is important to power system. It can be economic and reasonable to arrange start and stop of the Generator in wire net, The text adopt radial basis function neural networks. The GA-optimized multi-core radial basis function SVM is applied to extract useful data and short-term load forecasting accuracy based on RBF neural network has been improved. In this paper, The advantages of improving the algorithm is demonstrated by the application of the MATLAB simulation with the input data of the spring load collected from California, United States.

• Periodical: Advanced Materials Research (Volumes 614-615)
• Pages: 811-814
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.614-615.811
• Online since: December 2012
• Authors: Hong Zhang, Sheng Zhu Li, Luan Song Yue, Zhao Yu Pian
• Keywords: RBF Neural Network, Short-Term Load Forecasting, Support Vector Machine (SVM)

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