Short-term Net Load Forecasting Based on Temporal-spatial Feature Clustering and Two-layer Dynamic Graph Convolutional Network Modeling
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.
net load forecastingtemporal-spatial correlationtemporal-spatial features clusteringgraph convolutional networkdynamic graph structuretwo-layers
万方数据
维普
