Short-Term Load Prediction Based on Ant Colony Clustering-Elman Neural Network Model

In the application of neural network model for short term load prediction, main problems are over many training samples, long training time and low convergence speed. For representative training samples, an ant colony clustering model based on Elman neural network was proposed in this paper. First,...

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Bibliographic Details
Main Author: Dong-xing Duan
Format: Conference Proceeding
Language:English
Subjects:
ant colony
Artificial intelligence
clustering
Clustering methods
Convergence
Economic forecasting
Elman neural network
load prediction
Neural networks
Power engineering and energy
Power generation
Power generation economics
Power system modeling
Predictive models
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Summary:In the application of neural network model for short term load prediction, main problems are over many training samples, long training time and low convergence speed. For representative training samples, an ant colony clustering model based on Elman neural network was proposed in this paper. First, historical load data were pre-processed by using ant colony clustering method. The clustered data were chosen as training samples for the network. The objects are to make the input samples representative, decrease training time, increase convergence speed and improve prediction accuracy. Based on daily load data of one electric power plant, this model can obtained more accurate prediction results.
DOI:10.1109/WCSE.2009.695