Short-Term Photovoltaic Power Prediction for Microgrids Based on FCM–QPSO–LSTM

Traditional photovoltaic (PV) power prediction methods in microgrids are insensitive to weather characteristics, and fluctuations in weather data can lead to insufficient PV power prediction accuracy. Therefore, this paper proposes a short-term PV power prediction method for microgrids based on FCM–QPSO–LSTM, which is used to predict PV power under different seasons and weather types. First, the fuzzy [Formula: see text]-means clustering algorithm (FCM) is used to perform clustering analysis on multi-source weather data, and the initial dataset is clustered into sunny, cloudy, and rainy days. Second, to address the shortcomings of a single long-short-term memory (LSTM) model in terms of prediction accuracy and stability, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the model of the LSTM network, and the QPSO–LSTM prediction model is constructed. By introducing quantum dynamic inertia weights and adaptive learning factors, the QPSO algorithm enhances the global search capability and local search capability, which greatly improves the prediction accuracy and stability of the prediction model. Finally, the average absolute percentage error and root mean square error (RMSE) are applied as evaluation indexes to assess the performance of the prediction model. The simulation results show that the QPSO–LSTM model is better than the single LSTM model and the traditional PSO–LSTM model in terms of the average absolute percentage error and RMSE indicators and is able to make accurate predictions of short-term PV power under different seasons and weather types.