Data-driven modeling of solar power output using a CNN–LSTM approach

Short-term photovoltaic (PV) power forecasts are essential for storage dispatch, reserve scheduling, and grid safety, yet remain challenging under rapid irradiance ramps and seasonal regime shifts. We present a compact, causal CNN–LSTM architecture that couples local temporal pattern extraction with long-range sequence memory, augmented by physics-aware features (solar geometry, plane-of-array irradiance, clear-sky indices) and strict leakage safeguards. Using a one-hour-ahead task, we evaluate on a 2023 Accra, Ghana simulation study built with PVWatts v8 driven by NSRDB PSM v3. 2 (60 kWp DC, 55 kW AC). Metrics are reported in kW and normalized to DC capacity, with daylight/overall splits for fairness. The proposed model achieves RMSE = 0. 127 kW, MAE = 0. 092 kW, and R² = 0. 956 on the test split, reducing RMSE by 21. 6% vs. the best CNN-only variant and by 12. 4% vs. the best LSTM-only variant. Ablations show that engineered features and a Box–Cox target transform improve stability and accuracy; a 24 h look-back provides the best accuracy–latency balance, and moderate convolutional width (k=5, m=32) with d=128 LSTM units is near-Pareto-optimal (about 0. 093 M parameters and 2. 20 M MACs per step). Baselines (persistence, clear-sky-scaled smart persistence, and GBRT) are included to contextualize deterministic accuracy and skill. We also provide error anatomy by hour and season to highlight residual risks at dawn/dusk and during fast cloud transients. While results are strong, they reflect a simulation (plain PVWatts; no row-to-row shading or sensor noise). We outline a path to operational validation on measured plant AC data across seasons/sites and discuss extensions to probabilistic forecasting with calibrated intervals.