Mechanism-Dependent Antagonism of Auxiliary Information in Substation-Level Load Disaggregation for Distribution Network Planning

Abstract

Open-source energy system models disaggregate zonal electricity demand to substations through Voronoi-based preprocessing pipelines that combine socioeconomic weighting with auxiliary spatial corrections. Whether the same auxiliary data helps or harms when the weighting component shifts from rule-based to learned has not been investigated. We fix Voronoi partitioning and cross two design axes on metered demand from 1,891 British primary substations: the demand-weighting method and the mechanism through which Nighttime Light (NTL) intensity and substation-proximity signals enter the allocation, giving 15 configurations. Mechanism-isolation experiments further test additive post-correction and random-noise controls to pinpoint the structural cause of any performance reversal. The same auxiliary data reduces RMSE by 41 % on the static base but increases it by 21 % on the GNN base under multiplicative post-correction (p < 0.001 for both); the best static pipeline outperforms the best GNN variant by 19 %. Post-correction on the GNN improves rank-order correlation (p < 0.001) yet worsens absolute error, so correlation-only evaluation masks the calibration penalty. The isolation experiments trace this reversal to the multiplicative correction form under demand conservation constraints, not to signal redundancy; switching to additive post-correction eliminates the antagonism entirely. A transfer check on 13 German primary substations confirms directional replication and shows amplified antagonism where the GNN baseline already explains over 95 % of demand variance. The NTL and proximity signals behind the 41 % static improvement are publicly available at no cost and should be adopted as default corrections in static pipelines; method evaluation should report RMSE and correlation jointly, as the two metrics diverge under post-correction on learned representations.