Short Term Load Forecasting Based on Improved RBF Neural Network

Hong Zhang; Zhi Guo Lei; Jian Guo; Zhao Yu Pian

doi:10.4028/www.scientific.net/AMR.860-863.2610

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 860-863Short Term Load Forecasting Based on Improved RBF...

Short Term Load Forecasting Based on Improved RBF Neural Network

Abstract:

An improved radial basis function neural network is proposed that preprocessing is the key to improving the precision of short-term load forecasting. This paper presents a new model which is based on classical RBF neural network, combine the GA-optimized SVM radial basis function and RBF neural network. According to the date of the type, temperature, weather conditions and other factors ,The Application of combined GA-optimized SVM radial basis function is used to extract useful data to improve the load forecasting accuracy of RBF neural network. Spring load data of California were applied for simulation. The simulation indicates that the new method is feasible and the forecasting precision is greatly improved.

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Periodical:

Advanced Materials Research (Volumes 860-863)

Pages:

2610-2613

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.860-863.2610

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Online since:

December 2013

Authors:

Hong Zhang*, Zhi Guo Lei, Jian Guo, Zhao Yu Pian

Keywords:

Genetic Algorithm (GA), RBF Neural Network, Short-Term Load Forecasting, Support Vector Machine (SVM)

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