TransformGraph: A novel short-term electricity net load forecasting model

The development of the smart grid recognizes the importance of projecting electrical net load forecasting, which represents the difference between load demand and installed renewable energy sources (RES), such as wind and solar power. For net load forecasting, many approaches like statistical models, machine learning, and deep learning have been developed. Because of the significant uncertainty of RES, these models suffer from poor forecasting accuracy. In this study, we propose the TransformGraph, a novel Transformer model for electricity net load forecasting, which combines the Transformer and graph convolutional network (GCN). Firstly, the GCN is used as an input embedding layer to encode multivariate input sequences, which can fill the gap that the correlation information is not thoroughly considered in the Transformer. Then, the self-attention mechanism in the Transformer is used to capture the temporal dependence of the sequence data. Finally, the predicted load values are output using a feed forward neural network. Data from three countries derived from Open Power System Data (OPSD) sources are employed for the case study. Comparisons between the TransformGraph and the other five forecasting models demonstrate that the proposed one has higher forecasting accuracy and stability.