Short-term PV power prediction based on attention mechanism
Proposing a short-term PV power prediction model based on an attention mechanism addresses the challenges of tra-ditional PV power prediction,such as difficulty and low accuracy.The model utilizes historical data from photovoltaic power sta-tions for training,leveraging the local feature extraction capability of CNN and the sequential signal processing ability of BiLSTM.Additionally,the Attention mechanism allocates weight coefficients to different features.Simulating with data from a specific Aus-tralian photovoltaic power station,the Attention-CNN-BiLSTM model is compared with LSTM and other models,validating its supe-rior predictive accuracy.
short-term PV power forecastattention mechanismconvolutional neural network
万方数据
维普
