Traditional photovoltaic(PV)prediction models are highly susceptible to fluctuations in meteorological data and exhibit low sensitivity to meteorological features.To address this,we propose a short-term PV output prediction method based on multi-level feature extraction using bi-directional long short-term memory(BiLSTM),aimed at predicting PV output under various weather conditions.Firstly,meteorological factors with high correlation to PV output are selected as input features.The fuzzy C-means(FCM)clustering method is used for flexible sample division,and the Xie-Beni index is calculated to determine the optimal number of clusters,categorizing historical data into sunny,partly cloudy,cloudy,rainy,and severe weather conditions.Next,a multi-level feature extractor(MFE)comprising CNN-CBAM-TCN is constructed:convolutional neural networks(CNN)are employed for initial feature extraction,convolutional block attention module(CBAM)is used to suppress non-essential features,and temporal convolutional networks(TCN)are utilized to capture the temporal characteristics of intra-day PV output.Finally,BiLSTM is used for PV output prediction.Case studies validate the effectiveness of using the Xie-Beni index to determine the optimal number of clusters and demonstrate that this model achieves higher prediction accuracy compared to other prediction models under complex weather conditions.
short term photovoltaic output prediction/bi-directional short-term memory network/convolutional attention blocks/time convolutional network/fuzzy C-means clustering/Xie-Beni index
维普
万方数据