ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON NWP INFORMATION USING SSA OPTIMIZED EEMD-LSTM

Yishake&#x000b7;Simayi; Chen Hao; Zhang Zhengqiang; Xu Shuai; Liu Xinyi; Yu Lijun

doi:10.19912/j.0254-0096.tynxb.2024-0534

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 176-183. DOI: 10.19912/j.0254-0096.tynxb.2024-0534

ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON NWP INFORMATION USING SSA OPTIMIZED EEMD-LSTM

Yishake·Simayi¹, Chen Hao^1,2, Zhang Zhengqiang¹, Xu Shuai², Liu Xinyi³, Yu Lijun²

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Abstract

A combined wind power prediction model incorporating ensemble empirical mode decomposition （EEMD）, sparrow search algorithm （SSA）, and long short-term memory （LSTM） neural networks is proposed. The model decomposes the wind power time series through EEMD, overcoming the mode mixing phenomenon of traditional decomposition methods; Establish a mapping relationship between numerical weather prediction features covering meteorological variables such as wind speed, wind direction, air pressure, and humidity, as well as wind measurement tower information and power output; By using SSA algorithm to perform hyperparameter optimization on the prediction model based on LSTM neural network, the prediction accuracy of the model is significantly improved. The validation results demonstrate that this model outperforms other combined models, achieving improvements in root mean square error （RMSE） of 5.81% to 7.09%.

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wind power / forecasting / neural networks / modal decomposition / parameter optimization

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Yishake·Simayi, Chen Hao, Zhang Zhengqiang, Xu Shuai, Liu Xinyi, Yu Lijun. ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON NWP INFORMATION USING SSA OPTIMIZED EEMD-LSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 176-183 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0534

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