A short-term photovoltaic power generation prediction method based on multi-scale spatiotemporal correlation of arrays

Aiming at the problems that the power of photovoltaic power generation is greatly affected by meteorological factors, has strong volatility, and has low prediction accuracy, this paper proposes a short-term photovoltaic power generation prediction method based on the multi-scale spatiotemporal correlation of arrays. Fully considering the spatial correlation among photovoltaic arrays and the historical data characteristics at different time scales, a deep learning prediction model integrating graph convolutional networks, multi-scale convolution, and dual attention mechanisms was constructed. First, the topological associations and power propagation patterns among multiple arrays are captured through the spatial graph convolutional network. Second, a three-branch parallel convolutional structure is designed to extract multi-scale temporal features such as short-term fluctuations, medium-term trends, and long-term cycles. Third, a dual attention mechanism is introduced to achieve adaptive weighting of feature dimensions and time steps.; Finally, a multi-layer long short-term memory (LSTM) network is adopted for sequence modeling and power prediction. Verification based on the actual operation data of a large-scale photovoltaic power station for 12 consecutive months shows that, compared with hybrid deep learning baseline methods, the average absolute percentage error of the proposed method is reduced by ∼18.7% compared with the best baseline graph convolutional network [(GCN)-LSTM], and the coefficient of determination R2 reaches 0.934, effectively improving the short-term photovoltaic power prediction accuracy and providing reliable technical support for power grid dispatching, energy storage configuration, and energy management.