An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution

2018 ◽  
Vol 24 (10) ◽  
pp. 7534-7538
Author(s):  
Nurul Faezah Othman ◽  
Mohd Herwan Sulaiman ◽  
Zuriani Mustaffa
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Radial Basis

Short Term Load Forecasting Based on Improved RBF Neural Network

2013 ◽  
Vol 860-863 ◽  
pp. 2610-2613
Author(s):  
Hong Zhang ◽  
Zhi Guo Lei ◽  
Jian Guo ◽  
Zhao Yu Pian
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Short Term Load Forecasting ◽  
Radial Basis

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.

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

2012 ◽  
Vol 614-615 ◽  
pp. 811-814
Author(s):  
Hong Zhang ◽  
Sheng Zhu Li ◽  
Luan Song Yue ◽  
Zhao Yu Pian
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Input Data ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Matlab Simulation ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Short Term Load Forecasting ◽  
Radial Basis

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.

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