Prediction of electricity consumption based on genetic algorithm - RBF neural network

In order to avoid the economic loss due to too much or too little of electricity consumption, electricity consumption needs to be predicted. In order to solve the drawbacks of BP neural network, genetic algorithm and RBF neural network (GA-RBFNN) is presented to forecast electricity consumption in t...

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Bibliographic Details
Main Authors: Zeng Qing-wei, Xu Zhi-Hai, Wu Jian
Format: Conference Proceeding
Language:English
Subjects:
Biological cells
Economic forecasting
electricity consumption
Electronic mail
Energy consumption
Feedforward neural networks
genetic algorithm
Genetic algorithms
Neural networks
prediction
Predictive models
RBF neural network
Temperature
Weather forecasting
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Summary:In order to avoid the economic loss due to too much or too little of electricity consumption, electricity consumption needs to be predicted. In order to solve the drawbacks of BP neural network, genetic algorithm and RBF neural network (GA-RBFNN) is presented to forecast electricity consumption in the study, and genetic algorithm is introduced and tried in optimizing the parameters of RBF neural network. The electricity consumption data and relevant features data of a certain province from September to December in 2007 are used as the experimental data. The experiment results indicate that GA-RBFNN is very suitable for electricity consumption prediction by relevant features data.
DOI:10.1109/ICACC.2010.5487062