SHORT-TERM WIND POWER FORECASTING BASED ON ATTENTION-GRU WIND SPEED CORRECTION AND STACKING

Liu Shilin; Wang Deyi; Wang Wenkun; Liu Jing; Yang Guoqing

doi:10.19912/j.0254-0096.tynxb.2021-0712

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (12) : 273-281. DOI: 10.19912/j.0254-0096.tynxb.2021-0712

SHORT-TERM WIND POWER FORECASTING BASED ON ATTENTION-GRU WIND SPEED CORRECTION AND STACKING

Yang Guoqing^1,2, Liu Shilin¹, Wang Deyi^1,2, Wang Wenkun¹, Liu Jing^1,2

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Abstract

In view of the low accuracy of wind power forecasting due to the deviation of NWP wind speed and actual wind speed when using numerical weather prediction（NWP） data, this paper proposes a short-term wind power forecasting model based on Attention-GRU NWP wind speed correction and multi-algorithm fusion under Stacking framework. Firstly, the Pearson correlation coefficient between NWP prediction wind speed and actual wind speed is analyzed, and a wind speed correction model based on Attention-GRU is established to improve the precision of prediction wind speed. Secondly, considering the meteorological factors such as wind direction, temperature, humidity, air pressure and air density, a multi-algorithm wind power forecasting model based on Stacking framework is proposed, which integrates XGBoost, LSTM, SVR and LASSO. Grid search and cross-validation are used to optimize the model parameters. Finally, two typical wind farms in Northwest and Northeast China are selected to verify the model. The results show that the proposed model can improve the precision of NWP wind speed forecast and effectively improve the forecasting effect of wind power.

Key words

wind power / forecasting / weather forecasting / deep learning / ensemble learning

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Yang Guoqing, Liu Shilin, Wang Deyi, Wang Wenkun, Liu Jing. SHORT-TERM WIND POWER FORECASTING BASED ON ATTENTION-GRU WIND SPEED CORRECTION AND STACKING[J]. Acta Energiae Solaris Sinica. 2022, 43(12): 273-281 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0712

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