Spatial-Temporal Residential Short-Term Load Forecasting via Graph Neural Networks

• Published in: IEEE transactions on smart grid Vol. 12; no. 6; pp. 5373 - 5384
• Main Authors: Lin, Weixuan, Wu, Di, Boulet, Benoit
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Convolution; Correlation; Electric load forecasting; Electric power grids; Electrical loads; Forecasting; Graph neural networks; Houses; Load; Load forecasting; Load modeling; Machine learning; Neural networks; Predictive models; Residential energy; Spatial dependencies; spatial-temporal
• DOI: 10.1109/TSG.2021.3093515
• ISSN: 1949-3053

Electric load forecasting, especially short-term load forecasting, is of significant importance for the safe and efficient operation of power grids. With the wide adoption of advanced smart meters, more attention has been paid to short-term residential load forecasting. Most of the existing load forecasting methods are mainly focused on using temporal information of historical loads, and information of neighboring houses are generally ignored. However, houses in the same or neighboring areas may show similar consumption patterns due to shared conditions such as temperature, holiday impacts. Such information can be very helpful for machine learning based forecasting methods. In this paper, we propose to tackle the short-term residential load forecasting including both the individual load and aggregated load with a graph neural network based forecasting framework. The proposed framework can capture the hidden spatial dependencies of different houses without even any prior knowledge requirement on the geographic information for these houses. The proposed framework is evaluated on data sets of different residential houses from several areas. The experimental results demonstrate that the proposed framework can improve the residential forecasting accuracy by a wide margin compared with the baselines.