Application of Long Short-Term Memory Networks and SHAP Evaluation in the Solar Radiation Forecast

This paper proposes a hybrid forecasting framework that combines Long Short-Term Memory (LSTM) networks with Shapley Additive Explanations (SHAPs) to quickly and accurately predict solar radiation. Historical meteorological data from the Central Weather Administration (CWA) in Taiwan, spanning 2018–2023, are processed to construct multivariate input features, including temperature, humidity, pressure, wind conditions, global radiation, and temporal encodings. The LSTM network is employed to capture nonlinear dependencies and temporal dynamics in the multivariate meteorological data. SHAP-guided feature selection reduces the number of input variables, thereby lowering computational cost and accelerating convergence without sacrificing accuracy. A case study in the Penghu region—characterized by abundant solar irradiance and active photovoltaic deployment—was conducted to evaluate the model under three scenarios. Results demonstrated that if the number of features decreases from fifteen to five, the number of model parameters is reduced from 53,569 to 51,521 and the computation time is reduced from 6 ms to 4 ms. The MSE and MAE remain within the range of 0.07~0.11 and 0.13~0.18, with almost no change. The LSTM–SHAP framework not only achieves high forecasting precision but also provides transparent explanations of key meteorological drivers, with the temperature, humidity, and temporal variables identified as the most influential factors. Overall, this research contributes a scalable and interpretable methodology for solar radiation prediction, offering practical implications for photovoltaic power dispatch, grid stability, and renewable energy planning.