Peak load forecasting using the self-organizing map

Authors:
Shu Fan;Chengxiong Mao;Luonan Chen
Affiliations:
Osaka Sangyo University, Daito, Osaka, Japan;Huazhong University of Science and Technology, Wuhan, Hubei, China;Osaka Sangyo University, Daito, Osaka, Japan
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 2
Cited 2

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Abstract

This paper aims to study the short-term load forecasting of electricity by using an extended self-organizing map. We first adopt a traditional Kohonen self-organizing map (SOM) to learn time-series load data with weather information as parameters. Then, in order to improve the accuracy of the prediction, an extension of SOM algorithm based on error-correction learning rule is used, and the estimation of the peak load is achieved by averaging the output of all the neurons. Finally, as an implementation example, data of electricity demand from New York Independent System Operator (ISO) are used to verify the effectiveness of the learning and prediction for the proposed methods.