Ego-centric multiple-correlation and temporal graph neural networks based residential load forecasting

Accurate Residential Load Forecasting (RLF) is pivotal for the operation and decision-making in modern power systems. Recently, as the typical implementation and application of Artificial intelligence (AI) in energy field, Graph Neural Network (GNN) based RLF has emerged as a promising paradigm, since GNN can learn from graph-structured data, and capture complex interactions among nodes in a graph. However, it is challenging to build graphs that can effectively characterize the multiple unknown dependencies among residential users. To address the above issue, this paper proposes an effective residential load forecasting framework, based on intentionally constructed multiple ego-centric networks as well as multiple correlations and temporal graph neural networks. This work's contributions are given as follows. First, from two aspects: correlation and causality, multiple personalized ego-centric networks are intentionally constructed through data-mining manner, which respectively characterize the electricity consumption similarity between households, and direct influences on ego from the ego's neighbors (so-called alters) who essentially affect the ego's RLF. Second, multiple-correlation and temporal graph neural networks are adopted to forecast the ego's load. In detail, at each timestep, the ego node's hidden feature is embedded by multiple GNNs to represent multi-correlation dependencies between the ego and its alters, then the formed feature is sent to a recurrent neural network for further learning the spatial-temporal features. Finally, thorough experiments on real datasets demonstrate that our proposal outperforms the state-of-the-art spatial-temporal GNN-based forecasting schemes. Moreover, the empirical results verify that, for the load forecasting of single ego household, data-mining based personalized graphs can indeed significantly improve the forecasting accuracy, while the formulated personalized graphs are really sparsification and locality, which reflects the intuition that there are only relatively few useful relations in graphs based RLF. The source codes are available at https://github.com/tianxuHan/Residential-Load-Forecasting.