A Data-Driven Methodology for Scalable Distributed MPC in Heterogeneous Building Aggregation: From Systematic Feature Selection to Convex Optimization

Abstract

Coordinating large-scale, heterogeneous building aggregations for demand response (DR) is impeded by a dual challenge: the computational intractability of centralized Model Predictive Control (MPC) and the inadequacy of conventional feature selection methods, which fail to address the error-compounding nature of multi-step forecasting required by MPC. This paper proposes a comprehensive, data-driven framework that first employs a systematic, MPC-aware feature selection methodology to ensure robust multi-step prediction, then models the complex building dynamics using a novel Input-Convex Encoder-Only Transformer (IC-EoT) to guarantee a convex optimization problem, and finally solves the resulting constraint-coupled problem (CCP) in a fully distributed manner using the Tracking Alternating Direction Method of Multipliers (ADMM) algorithm. The framework is validated in a high-fidelity co-simulation environment, controlling a heterogeneous aggregation of consumer and prosumer buildings based on the EnergyPlus under a dynamic time-of-use (TOU) tariff. Results demonstrate that the proposed distributed approach achieves near-identical economic optimality and superior thermal comfort compared to a theoretical centralized controller, while exhibiting exceptional computational scalability that overcomes the real-time infeasibility of the centralized approach for large aggregations.