A dual-domain seasonal hybrid forecasting strategy for PV power considering dynamic uncertain fluctuations

The unpredictability of photovoltaic (PV) power output poses challenges to real-time demand matching, making accurate forecasting is crucial for better utilization of solar energy. To address the inherent uncertainty and intermittency, a dual-domain seasonal hybrid forecasting strategy for PV power is proposed. First, photovoltaic power data is classified by season and then the distinction between high dynamic features and low dynamic features is made through autocorrelation analysis. Moreover, an extended receptive field convolutional neural network (ERCNN) is developed for features extraction. In detail, a hierarchical architecture is established: integrating multi-head attention mechanism and modified input-output gates (MIOGBiLSTM-attention), a bidirectional long short-term memory (BiLSTM) is adopted for non-frequent fluctuation data, and a BiLSTM network model integrating multi-head attention mechanism and modified output gate (MOGBiLSTM-attention) is used for frequent fluctuation data. Furthermore, a temporal convolutional network (TCN) is introduced for residual error correction. A real PV dataset validates the model effectiveness, outperforming comparison models in forecasting accuracy and robustness.