Long-Term and Short-Term Photovoltaic Power Generation Forecasting Using a Multi-Scale Fusion MHA-BiLSTM Model

As the proportion of photovoltaic (PV) power generation continues to increase in power systems, high-precision PV power forecasting has become a critical challenge for smart grid scheduling. Traditional forecasting methods often struggle with accuracy and error propagation, particularly when handling short-term fluctuations and long-term trends. To address these issues, this paper proposes a multi-time scale forecasting model, MHA-BiLSTM, based on Bidirectional Long Short-Term Memory (BiLSTM) and Multi-Head Attention (MHA). The model combines the short-term dependency modeling ability of BiLSTM with the long-term trend capturing ability of the multi-head attention mechanism, effectively addressing both short-term (within 6 h) and long-term (up to 72 h) dependencies in PV power data. The experimental results on a simulated PV dataset demonstrate that the MHA-BiLSTM model outperforms traditional models such as LSTM, BiLSTM, and Transformer in multiple evaluation metrics (e.g., MSE, RMSE, R2), particularly showing stronger robustness and generalization ability in long-term forecasting tasks. The results prove that MHA-BiLSTM effectively improves the accuracy of both short-term and long-term PV power predictions, providing valuable support for future microgrid scheduling, energy storage optimization, and the development of smart energy systems.