Decision-focused end-to-end learning for enhanced building energy flexibility control

Abstract

Conventional control optimization methods for building energy systems typically adopt a two-stage decision-making paradigm, where predictive modeling and optimization are conducted in an independent and sequential manner. Such a paradigm assumes that the prediction accuracy would directly translate into decision quality, which may not always hold in practical engineering scenarios. To enhance the performance of model predictive controls in building energy management, this study proposes a novel decision-focused end-to-end learning method that transforms domain-specific knowledge into heuristic surrogate losses, thereby embedding downstream optimization objectives into the prediction model training process. Two physics-informed surrogate losses are designed for regulating building energy storage system operations, one weights prediction errors by their marginal contribution to operation costs considering price dynamics and grid interactions, while the other quantifies deviations in energy storage system operations from perfect foresight in terms of energy storage state trajectories and potential arbitrage opportunities. Comprehensive data experiments have been conducted using 102 40-day datasets collected from 9 buildings. The results demonstrate that the proposed methods can consistently outperform the conventional two-stage approach, achieving evident reductions in normalized regret by 6.7% to 28.7%. A counterintuitive finding reveals that the decision quality improves despite higher prediction errors, and the heuristic losses proposed can provide implicit regularization to ensure the generalization performance under unseen operating conditions. This study provides a novel perspective on developing computationally efficient, easy-to-implement, and interpretable control methods for enhanced building energy flexibility.