The net load is the difference between the actual load and the photovoltaic(PV)output.To address the chal-lenge of accurate forecasting which stems from the strong coupling between the fluctuating of actual load and the stochasticity of PV output,as well as the invisibility of PV output behind the meter,we put forward a short-term net load forecasting method based on temporal-spatial feature clustering and two-layer dynamic graph convolutional networks(GCN)modeling.Firstly,a net load subset clustering model is established by extracting daily temporal features,long-term trend features and spatial correlation features of user net loads.Secondly,a graph structure considering the"load-PV"bi-variate correlation is built with sub-clusters as graph nodes,to simultaneously reflect load and PV output characteristics.Finally,the total node of net load and the dynamic adjacency matrix are introduced,and a two-layer dynamic graph con-volution model connected by long short-term memory neural networks is constructed to obtain the net load forecasting results.The ablation experiments results based on the actual net load data from Ausgrid in Sydney indicate that the pro-posed spatio-temporal feature clustering method and the two-layer dynamic graph structure can reduce the root mean square error of net load forecasting results by 13.44 kW and 7.55 kW,respectively.Future work will further expand the forecasting scale and provide more information supports for power grid power supply decision-making.
关键词
净负荷预测/时空相关性/时空特征聚类/图卷积神经网络/动态图结构/双层
Key words
net load forecasting/temporal-spatial correlation/temporal-spatial features clustering/graph convolutional network/dynamic graph structure/two-layers
维普
万方数据