Temporal environment informed photovoltaic performance prediction framework with multi-spatial attention LSTM

Predicting the performance of photovoltaic (PV) systems is crucial for optimizing renewable energy utilization. However, traditional time-series methods focus only on temporal patterns, overlooking environmental variations, while dynamic conditions such as partial shading further complicate power prediction. To address this shading-induced variability, we propose a Temporal and Environment-Informed Prediction (TEIP) framework, which enhances PV power prediction by dynamically structuring temporal and environmental data through a novel multi-spatial attention LSTM (MSAL) network. This framework utilizes the TE matrix to capture structured environmental conditions over time, including the variability caused by partial shading. A dual-branch MSAL model uniquely processes environmental data through spatial feature extraction, which is then sequentially processed by LSTM to capture temporal dependencies. This hierarchical spatial–temporal processing enables dynamic adaptation to changing environmental conditions. Experimental results show the framework achieves superior prediction accuracy with R${}^2$ of 0.952 under sunny conditions, significantly outperforming traditional approaches. The framework demonstrates exceptional robustness by maintaining consistent performance (R${}^2$ of 0.948) even under challenging cloudy conditions, validating its effectiveness for real-world applications.