Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural Networks

Yingjie Liu and Mao Yang ()
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Yingjie Liu: Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China
Mao Yang: Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China

Energies, 2025, vol. 18, issue 15, 1-30

Abstract: Ultra-short-term PV power prediction (USTPVPP) results provide a basis for the development of intra-day rolling power generation plans. However, due to the feature information and the unpredictability of meteorology, the current ultra-short-term PV power prediction accuracy improvement still faces technical challenges. In this paper, we propose a combined prediction framework that takes into account the reconfiguration of the predictable components of PV stations and the spatiotemporal heterogeneous maps. A circuit singular spectral decomposition (CISSD) intrinsic predictable component extraction method is adopted to obtain specific frequency components in sensitive meteorological variables, a mechanism based on radiation characteristics and PV power trend predictable component extraction and reconstruction is proposed to enhance power predictability, and a spatiotemporal heterogeneous graph neural network (STHGNN) combined with a Non-stationary Transformer (Ns-Transformer) combination architecture to achieve joint prediction for different PV components. The proposed method is applied to a PV power plant in Gansu, China, and the results show that the prediction method based on the proposed combined spatio-temporal heterogeneous graph neural network model combined with the proposed predictable component extraction achieves an average reduction of 6.50% in the RMSE, an average reduction of 2.50% in the MAE, and an average improvement of 11.93% in the R 2 over the direct prediction method, respectively.

Keywords: circulant singular spectrum decomposition; predictable component extraction and reconstruction; STHGNN-Ns-Transformer; ultra-short-term photovoltaic power prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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