Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

Abstract:

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.

• Periodical: Advanced Materials Research (Volume 933)
• Pages: 384-389
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.933.384
• Online since: May 2014
• Authors: Xin Zhao*, Shuang Xin Wang
• Keywords: BP Neural Network (BPNN), Short-Term Prediction, Small-World Optimization Algorithm, Wind Power Forcasting

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