Abstract:

To address the issue that traditional residential overall power load forecasting methods fail to fully consider the differences in users' electricity consumption habits, making it difficult to improve prediction accuracy, a residential net load forecasting method combining representation learning clustering and an AGCN-Transformer was proposed. The representation learning method was utilized to fully extract the latent features of users' net load data, and users were divided into different groups based on the similarity of electricity consumption behaviors; the net load data of the same group were aggregated, and a graph structure suitable for this task was constructed by comprehensively considering the number of clusters, users' historical net load sequences, and the correlations among different groups; the AGCN-Transformer model was adopted to predict the future loads for users of different categories. Experimental results indicate that the proposed method outperforms multiple mainstream forecasting models in both prediction accuracy and generalization ability, showing a good application prospect.

Key words: residential area, representation learning clustering, adaptive graph neural network, Transformer, net load forecasting