电力系统保护与控制2024，Vol.52Issue(14)：82-94,13.DOI:10.19783/j.cnki.pspc.231395
考虑阵列间时空相关性的超短期光伏出力预测
Ultrashort-term photovoltaic output forecasting considering spatiotemporal correlation between arrays
摘要
Abstract
Photovoltaic(PV)output forecasting is crucial for optimizing power grid dispatching and enhancing new energy consumption,especially with the rapid development of the PV industry in China.To capture the spatial correlation among different arrays in a PV site and the temporal dynamics of PV power outputs,a novel ultra-short-term PV output forecasting method based on a graph convolutional network and long short-term memory(GCN-LSTM)network is proposed.The proposed method first constructs a graph model to represent the connection relationships of different arrays on the PV site.Then the graph convolutional network is used to extract spatial features from the graph model to obtain time series data that incorporate the spatial relationships among different arrays.Finally,time series data is input into the LSTM network to perform PV output prediction.Experiments demonstrate that the GCN-LSTM-based PV output forecasting method achieves high accuracy and stability,which makes up for the inherent limitations of prediction methods based on time series data and shows promising application potential in large-scale power plants.关键词
光伏发电/超短期预测/时空相关性/图卷积神经网络/长短期记忆网络Key words
photovoltaic power generation/ultra-short-term forecasting/spatio-temporal correlation/graph convolution network/long short-term memory network引用本文复制引用
韩晓,王涛,韦晓广,王军..考虑阵列间时空相关性的超短期光伏出力预测[J].电力系统保护与控制,2024,52(14):82-94,13.基金项目
This work is supported by the National Key R&D Program of China(No.2021YFB2601500). 国家重点研发计划项目资助(2021YFB2601500) （No.2021YFB2601500）
成都市科技局揭榜挂帅科技项目资助(2023-JB00-00002-SN) （2023-JB00-00002-SN）
