A Hybrid 1D-CNN–LSTM–MHA Model for Short-Term Probabilistic Photovoltaic Power Forecasting

The intermittent nature of photovoltaic power generation makes short-term forecasting critical for grid management. In this study, a hybrid model combining a one-dimensional convolutional neural network (1D-CNN), Long Short-Term Memory (LSTM), and multi-head attention (MHA) mechanism was developed and evaluated on a 300-day dataset at 15 min (H1) and 60 min (H4) forecast horizons. The model generates prediction intervals with 80% nominal coverage using a quantile regression approach trained with the pinball loss function. The hyperparameters were determined through Optuna-based Tree-structured Parzen Estimator (TPE) optimisation. The LSTM, 1D-CNN-LSTM, LSTM-MHA, and 1D-CNN-LSTM-MHA models were compared under the same experimental setting. The 1D-CNN-LSTM-MHA model achieved the best deterministic performance at both forecast horizons. At the H1 horizon, R2 = 0.9370 and nRMSE = 7.13% were obtained, whereas at the H4 horizon, R2 = 0.9327 and nRMSE = 7.37% were achieved. In the probabilistic evaluation, this model produced the lowest PINAW and Winkler score values. In the statistical comparison, the performance differences in the 1D-CNN-LSTM-MHA model relative to the LSTM reference model were statistically significant in the DM-MAE, DM-RMSE, Clark–West, and Fisher-Z tests at both horizons. The statistical results indicate that the contribution of the attention mechanism becomes more evident when combined with the convolutional component, whereas adding attention alone to the LSTM did not produce a statistically significant improvement. This study provides a comparative evaluation framework for short-term PV active power forecasting by combining probabilistic forecasting through quantile regression with statistical model comparison.