Deep learning prediction models for short-term solar photovoltaic power generation forecasting

The increasing concerns about the environmental impact of fossil fuels have emphasized the importance of clean solar energy, which offers a pollution-free alternative for meeting growing energy needs. However, the accurate prediction of solar photovoltaic (SPV) based power generation is a very challenging task because of its inherent variability and uncertainty. To address this challenging problem, this paper applies several machine-learning, deep-learning, and their hybrid models such as: One-Dimensional Convolutional Neural Network (1D CNN), Bi-Directional Long Short-Term Memory (Bi-LSTM), Stacked LSTM, Artificial Neural Network (ANN), Linear Regression (LR), Support Vector Regression (SVR), XGBoost, and a hybrid CNN-LSTM model. These models are examined and compared on four different data sequences of DKASC Alice Springs dataset. The prediction performances of all these models are evaluated based on various error metrics: MAE (mean absolute error), explained variance, RMSE (root mean square error), R², and sMAPE (symmetric mean absolute percentage error). The simulation results demonstrates that Stacked LSTM model outperforms all other benchmark forecasting models and able to obtains average values of performance metrics i.e. MAE of 1.1157, RMSE of 2.3408, an Explained Variance of 0.8998, R² of 0.9004, and sMAPE of 1.1795 as evaluated across all four different data sequences. Moreover, a comprehensive statistical analysis, using Diebold Mariano Test and boxplots, confirms the further superiority of Stacked-LSTM model to efficiently address inherent uncertainty of solar power generation.