ULTRA SHORT TERM POWER PREDICTION OF PHOTOVOLTAIC POWER GENERATION BASED ON VMD-LSTM AND ERROR COMPENSATION

Wang Fuzhong; Wang Shuaifeng; Zhang Li

doi:10.19912/j.0254-0096.tynxb.2021-0043

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 96-103. DOI: 10.19912/j.0254-0096.tynxb.2021-0043

ULTRA SHORT TERM POWER PREDICTION OF PHOTOVOLTAIC POWER GENERATION BASED ON VMD-LSTM AND ERROR COMPENSATION

Wang Fuzhong, Wang Shuaifeng, Zhang Li

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Abstract

The high complexity of photovoltaic sequences has a great impact on the prediction accuracy of photovoltaic power generation. Therefore, an ultra-short-term power prediction model of photovoltaic power generation based on VMD-LSTM and error compensation is proposed. In the first stage of the model,the VMD algorithm is used to decompose the original power sequence into several different modes,and the corresponding LSTM network model is established for prediction,and the initial predicted power is obtained by summing the prediction results of each mode;In the second stage, the LSTM network is used to perform error compensation prediction on the error sequence,and then the initial prediction power and the error prediction power are summed to get the final prediction result. The simulation results show that the prediction model has high adaptability to the weather, and the prediction accuracy is over 97%.

Key words

PV power generation / power forecasting / deep learning / long short-term memory / variational mode decomposition / error compensation

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Wang Fuzhong, Wang Shuaifeng, Zhang Li. ULTRA SHORT TERM POWER PREDICTION OF PHOTOVOLTAIC POWER GENERATION BASED ON VMD-LSTM AND ERROR COMPENSATION[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 96-103 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0043

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