Bridging accuracy and interpretability in solar forecasting: A regional ensemble learning approach

Accurate solar irradiance forecasting is critical for optimizing photovoltaic systems, enhancing grid stability, and supporting energy storage management, particularly in regions with high climatic variability like Saudi Arabia. However, existing models often lack either the precision to handle dynamic atmospheric conditions or the interpretability needed for operational trust. This study addresses these challenges by developing a modular solar forecasting dashboard that balances accuracy and transparency for five climatically diverse Saudi cities: Riyadh, Jeddah, Abha, Dammam, and Qassim. The proposed framework combines Random Forest regression with physics-informed feature engineering (e.g., hour-cloud interactions) and Shapley Additive Explanations to ensure interpretable predictions. Empirical results demonstrated robust performance, with determination coefficients exceeding 0.90 across all regions and root mean squared errors as low as 92.4 watts per square meter in Dammam. A residual-based alert system flagged significant deviations ( ± 100 watts per square meter), revealing Riyadh’s high alert rate (32.2%) due to dust-induced variability. Feature analysis confirmed the dominance of temporal-cloud interactions, accounting for 38%–42% of prediction variance. The study provides a scalable, interpretable solution for solar forecasting, aligning with Saudi Vision 2030’s renewable energy goals. Future work will integrate real-time satellite data and adaptive alert thresholds to further enhance operational utility. • Achieved coefficient of determination above 0.90 and error as low as 92 W/m 2 . • Feature analysis showed temporal-cloud interactions drive 38%–42% of variance. • Alerts identified high-variability days, with Riyadh showing a 32% alert rate. • Coastal cities (e.g., Dammam) had more stable forecasts than mountainous regions. • Physics-informed features improved accuracy by 12% over traditional inputs.