ULTRA-SHORT-TERM PHOTOVOLTAIC POWER PREDICTION USING MHA-LSTM CONSIDERING NWP SIMILAR DAYS

Ye Jianying; Liu Zhaoqi; Li Zhouchen; Lin Bo; Liu Lei; Chen Yingting

doi:10.19912/j.0254-0096.tynxb.2024-1857

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 132-139. DOI: 10.19912/j.0254-0096.tynxb.2024-1857

ULTRA-SHORT-TERM PHOTOVOLTAIC POWER PREDICTION USING MHA-LSTM CONSIDERING NWP SIMILAR DAYS

Ye Jianying, Liu Zhaoqi, Li Zhouchen, Lin Bo, Liu Lei, Chen Yingting

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Abstract

To address the issue of insufficient accuracy in ultra-short-term power prediction using historical data in the field of photovoltaic power generation, an ultra-short-term PV power prediction method is proposed, which integrates numerical weather prediction （NWP） similarity day and multi-head attention-long and short-term memory network （MHA-LSTM）. The correlation coefficient method is used to analyze the inherent relationship between PV power and meteorological information, combing the meteorological factors that are highly correlated with PV power in the historical data, constructing the variable matrix of key meteorological information, and carrying out the segmentation process in order to carry out a more fine-grained analysis of similar days. A multi-head self-attention mechanism is introduced to automatically capture the complex functional relationships embedded in the NWP data, and the data information enhanced by the multi-head self-attention mechanism is processed by using a long- and short-term memory neural network （LSTM） to obtain more accurate ultrashort-term prediction results. Finally, a number of different weather types are selected to compare and analyze the prediction accuracy of the algorithm, and the experimental results show that the prediction method proposed in this paper achieves a significant improvement in prediction accuracy compared with the traditional algorithm, which verifies the effectiveness and accuracy of the method.

Key words

photovoltaic power generation / neural networks / long short-term memory / power prediction / self-attention mechanism / similar day selection

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Ye Jianying, Liu Zhaoqi, Li Zhouchen, Lin Bo, Liu Lei, Chen Yingting. ULTRA-SHORT-TERM PHOTOVOLTAIC POWER PREDICTION USING MHA-LSTM CONSIDERING NWP SIMILAR DAYS[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 132-139 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1857

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