A Novel Spatiotemporal Pyramidal Graph Modeling Approach for Short-Term Residential Load Forecasting

Precise short-term residential load forecasting (STRLF) is essential for maintaining stable and cost-effective operations on the demand side. Both spatial and temporal information are important for the STRLF tasks, but effectively extracting them remains a significant challenge due to the highly volatile and stochastic nature of residential consumption patterns. To this end, this article proposes pyramidal attention (PATNet), a low-complexity transformer network with spatiotemporal PATNet, to explore multiresolution spatiotemporal representations of residential load series and forecast multiple residential loads several steps ahead. Specifically, the temporal and spatial patterns of residential load series are, first, formulated as a temporal pyramidal graph and a spatial pyramidal graph according to the periodic characteristics of load time series and the spatial correlations of different residential units, respectively. Two types of low-complexity attention mechanisms—temporal and spatial PATNet—are, then, specifically designed for the temporal and spatial pyramidal graphs such that the short- and long-range temporal dependencies and dynamic spatial correlations among various groups of residents can be captured. Moreover, to enhance multistep forecast performance, we design a gated fusion unit that is capable of adaptively fusing extracted spatiotemporal information and a transform attention block that can translate historical loads into future forecasts. Numerical simulations using several real-world residential load datasets demonstrate that the proposed framework outperforms state-of-the-art load prediction methods by 7.98% at least in single-step forecasting and 11.38% at least in multistep forecasting.