Deep learning for solar power forecasting: A robust stacked LSTM algorithm for operational applications

Accurate short-term forecasting of photovoltaic (PV) power is essential for reliable grid operation and renewable integration. We propose a stacked Long Short-Term Memory (LSTM) network to predict one-hour-ahead PV output for a 1 kWp crystalline-silicon system using PVGIS-SARAH3 hourly data (2005–2023) at a central Iran location. After timestamp parsing, hourly resampling, interpolation, and min–max normalization, 24-hour sliding windows form the model inputs. Our architecture two LSTM layers of 50 units each followed by a single Dense output neuron, was trained (20 epochs, batch ≈ 3000, early stopping patience = 0) on 70% of the data and tested on the remaining 30%. Evaluation on unseen data yields RMSE = 0.084 kWp, MAE = 0.065 kWp, MAPE = 11.7%, and R² = 0.88, corresponding to a 22% RMSE reduction versus a persistence baseline. Detailed error analysis (scatter, residual histogram, hourly MAE) highlights systematic underestimation at high irradiance and late-afternoon variability. These results demonstrate that our simple, easily-implemented LSTM achieves performance on par with more complex deep-learning frameworks, making it suitable for rapid deployment in operational forecasting systems.