【摘要】 随着我国光伏产业建设步伐的加快，光伏出力预测对于优化电网调度和提高新能源消纳的意义日益凸显。基于光伏站点中不同阵列之间的空间相关性和光伏功率输出的时序特性，提出一种基于图卷积神经网络和长短期记忆网络(graph convolution network and long short-term memory, GCN-LSTM)的超短期光伏出力预测方法。该方法首先以图的形式刻画出光伏站点中不同阵列的连接关系。然后利用图卷积神经网络(graphconvolutionnetwork,GCN)实现图模型的空间特征提取，并得到包含不同阵列之间空间特征的时序信息。最后将时序数据输入长短期记忆网络(long short-term memory, LSTM)进行光伏出力预测。实验结果表明，基于GCN-LSTM的光伏出力预测方法具有较高的精确性与稳定性，在一定程度上弥补了基于时序信息预测方法的固有缺陷，并且展现出在大规模电站上的良好应用前景。更多还原

【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.This work is supported by the National Key R & D Program of China(No. 2021YFB2601500).更多还原