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

Seyed Ahmadreza Dehghanian (), Sahand Heidary (), Danial Shams (), Ali Mastali Pour () and Rahim Zahedi ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 10, 1591-1600

Abstract: 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.

Keywords: Deep learning; Error analysis; LSTM; PVGIS-SARAH3; Photovoltaic; Solar forecasting; Time series. (search for similar items in EconPapers)
Date: 2025
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