Photovoltaic Power Forecasting: Benchmarking Deep Learning Architectures for Scenario-Based Production Scheduling

Abstract

Accurate short-term photovoltaic (PV) power fore-casting is important for energy-aware production planning in manufacturing environments integrating renewable sources. This paper benchmarks six deep learning architectures—LSTM, BiLSTM, GRU, CNN–LSTM, CNN–BiLSTM, and CNN–GRU—for PV power forecasting at 30-minute resolution under a strictly controlled protocol (identical data split, preprocessing, input window, and evaluation metrics). Experiments on a real annual PV dataset show that CNN–LSTM achieves the best RMSE in the univariate setting (PV power only) with RMSE = 1.153 and R = 0.972, while LSTM attains the lowest MAE (MAE = 0.564). In a multivariate extension (PV + weather), CNN–LSTM remains the best hybrid model (RMSE = 1.148, R = 0.969), with only marginal RMSE improvement over the univariate CNN– LSTM. Beyond point forecasting, the proposed pipeline converts the best model output into planning-ready uncertainty inputs by generating Monte Carlo trajectories from validation residuals and reducing them into compact multi-day scenarios with probability weights over a weekly horizon. These weighted scenarios are designed to be directly injected as PV availability inputs in uncertainty-aware production scheduling and energy management.