A physical virtual multi-graph convolutional coordinated prediction method for spatio-temporal electricity loads integrating multi-dimensional information

Abstract

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Traditional load prediction methods are unable to effectively predict the loads according to the spatial topology of each electricity consumer in neighboring areas and the load dependency correlations. In order to further improve the load prediction accuracy of each consumer in the region, this paper proposes a short-term prediction method of electric load based on multi-graph convolutional network. First, the input data are selected with maximum information coefficient method by integrating multi-dimensional information such as load, weather, electricity price and date in the areas. Then, a gated convolutional network is used as a temporal convolutional layer to capture the temporal features of the loads. Moreover, a physical-virtual multi-graph convolutional network is constructed based on the spatial location of each consumer as well as load dependencies to capture the different evolutionary correlations of each spatial load. Comparative studies have validated the effectiveness of the proposed model in improving the prediction accuracy of power loads for each consumer.

Keywords

• graph convolutional network
• short-term load
• multidimensional information
• spatiotemporal prediction
• maximum information coefficient