Load forecasting is no longer only a point-prediction problem for aggregate demand. In renewable-rich and electric-vehicle-intensive power systems, forecasts must support net-load balancing, charging-demand management, uncertainty-aware operation, and spatially coupled decision-making. This review presents a quantitative evidence map based on a curated DOI-linked corpus of 116 papers published between 1960 and 2026. Each paper is coded by dominant model family, application theme, forecast horizon, and frontier feature tags. Publication era and dominant model family are strongly associated (χ2(21)=93.69, p=3.70×10−11, Cramérś V=0.519). Post-2020 studies are sharply enriched in transformer/graph-neural-network/foundation-model content (13/43 versus 0/73; Haldane-corrected odds ratio 65.07; Fisher p=6.65×10−7), electric-vehicle or charging themes (7/43 versus 0/73; odds ratio 30.21; p=6.91×10−4), and deep-learning content (14/43 versus 7/73; odds ratio 4.36; p=2.76×10−3). To address category coarseness, the frontier family is further decomposed into transformer-only, graph-neural-network-only, hybrid spatiotemporal, and foundation-model subfamilies. The central conclusion is that the most important forecasting topic for current electrical power systems is not generic short-term load forecasting, but the integrated forecasting stack required by electrified, renewable-rich, and spatially coupled grids.
Jaramillo et al. (Tue,) studied this question.