The Council of Experts: A Heterogeneous Rational-Aware Framework for Robust Energy Forecasting

Abstract

Accurate forecasting of building energy consumption is a cornerstone of modern smart grid management and sustainable facility operations. However, standard deep learning approaches—such as Long Short-Term Memory (LSTM) networks and Transformers—often function as black boxes, failing to explicitly model the governing physical laws and thermodynamic constraints of building systems. This limitation frequently results in poor generalization and instability when applied to diverse building portfolios. To address this challenge, we propose a novel Rational-Aware Architecture, a heterogeneous Mixtureof-Experts (MoE) framework that decomposes the forecasting task into specialized semantic agents. The architecture comprises a Thermodynamicist (Physics-ResNet) to model enthalpy and heat transfer, a Meteorologist (WeatherCNN) to capture environmental gradients, a Manager (Time2Vec) to encode temporal cyclicities, and an Engineer (Sequence Model) to handle historical load inertia. A context-aware gating mechanism dynamically weighs these experts based on specific building characteristics. Extensive experiments on a large-scale dataset of 800 buildings from the ASHRAE Great Energy Predictor III challenge demonstrate that the proposed framework significantly outperforms standard deep learning baselines. The Rational-Aware Transformer achieved a Mean Absolute Percentage Error (MAPE) of $\mathbf{1 9. 9 7 \%}$, representing a relative error reduction of approximately 40% compared to the standard Transformer baseline (33.03%). Furthermore, the Rational-LSTM variant demonstrated exceptional stability with an $R^{2}$ score of 0.9452, effectively mitigating the volatility often observed in pure data-driven approaches. These results confirm that integrating domain knowledge into deep learning architectures yields superior robustness, precision, and interpretability for energy forecasting tasks.