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International Conference on Electronics, Information and Communication Engineering (EICE 2012)

Garry Lee
Garry Lee
Information Engineering Research Institute
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ASME Press
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The load forecast level in power system is a important symbol to measure operations and management of power system. This paper summarized the research conditions of the short-term load forecasting using artificial neural network method, and analyzed the characteristics of electrical load and factors of influencing power load forecasting accuracy. The paper used the particle swarm optimization neural network method in short-term load forecasting of power grid. Based on the analysis history loads in California power system, we established the load forecasting model considering the various affecting factors, and normalized the input load, meanwhile quantified date, atmosphere and other factors. The example showed that the model of neural network based on the particle swarm optimization algorithm can improve the prediction precision and speed, it's performance prediction is superior to the model based on BP neural network load forecasting.

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