Weather-Dependent Photovoltaic Energy Prediction via Hybrid Deep Learning Models for Sustainable Energy Management

Accurate photovoltaic (PV) power forecasting is pivotal for facilitating the integration of renewable energy into modern power systems and supporting sustainable energy development. However, existing methods often rely on single deep learning architectures, require complex preprocessing, suffer from training instability, and lack the ability to capture long-range temporal dependencies. To address these issues, this study develops and compares two hybrid deep learning models—ConvTempNet and DilaTransNet—for hourly PV energy prediction using meteorological and temporal data from two Portuguese PV stations. Quantitative results show that the optimized ConvTempNet achieves superior hourly predictive accuracy with an hourly RMSE of 1.16 kWh and an R2 of 0.95 at Tartaruga (2.66 kWh, R2 = 0.95 at Zarco). Systematic evaluations were conducted, including dropout ablation (a systematic test of different dropout rates to assess model robustness and regularization effects) (0.2–0.4), performance assessment using RMSE, R2, MAE, and MAPE, and sensitivity analysis to assess predictive accuracy and variable importance. Results show that the optimized ConvTempNet yields superior hourly accuracy with an hourly RMSE = 1.16 kWh and an R2 = 0.95 at Tartaruga (2.66 kWh, R2 = 0.95 at Zarco). The tuned DilaTransNet shows stronger robustness to moderate dropout. Solar radiation is the dominant input variable, while temperature, humidity, and hour affect the two models differently. The two models exhibit complementary strengths, supporting site-specific parameter optimization for reliable PV forecasting.