SHORT-TERM PV POWER PREDICTION BASED ON POWER FLUCTUATION FEATURES AND SSA-GRU JOINT OPTIMIZATION

Ma Yiwei; Ma Weixing

doi:10.19912/j.0254-0096.tynxb.2025-0512

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 161-168. DOI: 10.19912/j.0254-0096.tynxb.2025-0512

SHORT-TERM PV POWER PREDICTION BASED ON POWER FLUCTUATION FEATURES AND SSA-GRU JOINT OPTIMIZATION

Ma Yiwei¹, Ma Weixing^1,2

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Abstract

The random fluctuation characteristics of PV power are an important reason for low prediction accuracy. Therefore, this paper proposes a short-term PV power prediction method that combines PV power fluctuation features and joint optimization of SSA-GRU model. Firstly, four different PV power fluctuation feature models and an FCM-based clustering algorithm for similar power fluctuation patterns are built to obtain various input sub datasets with good distribution characteristics. Secondly, based on PV power fluctuation features, an SSA-GRU prediction model is constructed by integrating singular spectrum analysis （SSA） and gated recurrent unit （GRU）, and an improved coati optimization algorithm （ICOA） is given to jointly optimize the model to fully utilize their synergistic advantages for improving PV power prediction performance. To verify the superiority of the proposed method, a comprehensive comparative experiment is conducted using data from a real PV power station in Ningxia. The results fully demonstrate that the proposed method is scientifically effective and has better predictive performance than comparative models.

Key words

PV power / prediction / optimization / power fluctuation feature / singular spectrum analysis / gated recurrent unit network

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Ma Yiwei, Ma Weixing. SHORT-TERM PV POWER PREDICTION BASED ON POWER FLUCTUATION FEATURES AND SSA-GRU JOINT OPTIMIZATION[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 161-168 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0512

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