Abstract
The accurate wind power load forecasting has important significance for power production, power network safe operation and national economy. By comprehensively analyzes the advantages and disadvantages of various forecasting method, combining grey forecast and neural network training by particle swarm optimization, this paper establishes combined forecast model based on gray neural network trained by particle swarm optimization and applies it into short-term load forecasting of wind power. Empirical analysis shows that this method is science and practical.
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Niu, D., Wei, Y., Qiao, H., Fang, F. (2011). Combined Forecast for Wind Power Short-Term Load Based on Gray Neural Network Trained by Particle Swarm Optimization. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23753-9_61
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DOI: https://doi.org/10.1007/978-3-642-23753-9_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23752-2
Online ISBN: 978-3-642-23753-9
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