Comparison of Three Short Term Wind Power Forecasting Methods

Abstract:

An accurate forecasting method for wind power generation of the wind energy conversion system (WECS) is urgent needed under the relevant issues associated with the high penetration of wind power in the electricity system. This paper presents a comparison of three forecasting approaches on short term wind power generation of WECS. Three forecasting methods, namely, persistence method, back propagation neural network method, and radial basis function (RBF) neural network method, are investigated. To demonstrate the performance of three methods, the methods are tested on the practical information of wind power generation of a WECS. The performance is evaluated based on two indexes, namely, maximum absolute error and mean absolute error.

• Periodical: Advanced Materials Research (Volume 684)
• Pages: 671-675
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.684.671
• Online since: April 2013
• Authors: Wen Yeau Chang
• Keywords: Back Propagation (BP) Neural Network, Persistence Method, Radial Basis Function Neural Network, Wind Energy Conversion System, Wind Power Generation Forecasting

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