Physics-Guided Dynamic Prediction and Intrinsic Interpretability of Substation Carbon-Emission Factors: A MOIRAI-2 and UPINN Fusion Framework

Substation-level carbon-emission factors (CEFs) are operationally relevant because substations concentrate transformer losses, auxiliary consumption, and sulfur hexafluoride (SF6) leakage at the interface between transmission and distribution. However, static or annual emission-factor methods average over heterogeneous operating regimes and cannot capture the pronounced non-stationarity of substation CEFs driven by seasonal loads, stochastic maintenance events, cooling-system switching, and extreme weather. To support high-frequency dynamic carbon tracing, dispatch optimization, and audit compliance, this study proposes a physics-guided fusion framework integrating a temporal foundation model, MOIRAI-2, with a Uniform Physics-Informed Neural Network (UPINN). A 15-dimensional physically constrained feature vector is constructed from IEEE C57.91 thermal-circuit equations and ideal-gas state equations, including transformer top-oil/hot-spot temperature, SF6 pressure/density estimation, and oil-forced/air-forced (OFAF) or oil-directed/water-forced (ODWF) cooling status. MOIRAI-2 uses Any-Variate Attention with binary attention bias to model intra-variate temporal dependencies and cross-variate physical couplings, whereas UPINN embeds thermal-balance, SF6 leakage-kinetics, and CEF conservation residuals as soft constraints. An adaptive gating network balances data-driven pattern recognition and physics-driven smoothness across steady-state, extreme-event, and maintenance regimes. Validation on a 220 kV substation dataset achieves a mean absolute error (MAE) of 0.352 gCO₂e/kWh, outperforming random forest (RF), gradient boosting machine (GBM), long short-term memory (LSTM), and a Pure Transformer by 24.0%, 19.1%, 23.0%, and 14.4%, respectively. Ablation studies show that the 15-dimensional physical-feature expansion improves accuracy by 8.8%, whereas physics-loss regularization reduces prediction variance by 37%. UPINN decomposition further indicates that transformer total loss, ambient temperature, and load factor dominate CEF dynamics, and rainfall cooling reduces CEF by 0.04 gCO₂e/kWh per 20 mm increment. The framework provides a physically consistent and intrinsically interpretable basis for dynamic substation carbon accounting and low-carbon operation.