A Relation-Aware Multi-Driver Pipeline for Interpretable Low-Frequency Load Disaggregation Under Partial Observability

Non-intrusive load monitoring (NILM) estimates component-level energy use from aggregate measurements, but low-frequency data limit appliance signatures and make overlapping or weakly observed loads difficult to separate. This paper proposes a relation-aware multi-driver pipeline for interpretable low-frequency load attribution under partial observability. The method does not require supervised component labels or predefined appliance models. It combines semantic feature typing, heterogeneous relation discovery, feature-family construction, mechanism-aware evidence modeling, conservative allocation, event-background separation, and role-based attribution. Only evidence-supported load is assigned to feature families, while unsupported variation is retained as unexplained demand or residual load. The method is evaluated in a simulated EV-focused building case and through measured-building validation on nine ADRENALIN buildings. In the EV case, the selected EV-aligned family achieved a correlation of 0.990 and an NMAE of 0.100 against the withheld EV reference, while heat-pump and base-load recovery was weaker, with NMAE values of 0.565 and 0.895. In the ADRENALIN validation, temperature-associated families achieved median NMAE values of 0.594 using the restricted feature set and 0.576 using the full feature set. Additional comparison, ablation, sensitivity, diagnostic, and runtime analyses show that the pipeline is most effective for dominant event-driven loads, remains limited for smoother or masked lower-magnitude components, and treats unexplained variation explicitly. The results demonstrate a practical framework for interpretable driver-based load attribution when component labels are unavailable or incomplete.