Application of radial basis function neural network model for short-term load forecasting

1995 ◽  
Vol 142 (1) ◽  
pp. 45 ◽  
Author(s):  
D.K. Ranaweera
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Network Model ◽  
Neural Network Model ◽  
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.

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

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