Scalable Distributed Learning-Based MPC for Heterogeneous Consumer-Prosumer Building Aggregations: MPC-Aware Feature Selection and Convex Constraint-Coupled Optimization

Abstract

Buildings provide demand-response flexibility through controllable thermal loads and thermal inertia, while distributed photovoltaic deployment creates opportunities to share surplus generation across heterogeneous consumer-prosumer aggregations. This study develops a scalable distributed learning-based model predictive control (MPC) framework that preserves convexity to enable a fully distributed optimizer with an asymptotic global-optimality guarantee. The Input-Convex Encoder-Only Transformer (IC-EoT) is embedded through recursive substitution to preserve local MPC convexity, while the aggregation-wide energy-trading balance yields a convex constraint-coupled problem solved by Tracking Alternating Direction Method of Multipliers (Tracking-ADMM) with bounded-neighbour communication and fully distributed finite-iteration stopping. An MPC-aware feature-selection procedure additionally accounts for future-value availability and recursive error propagation. In EnergyPlus-Energym co-simulation, centralized MPC becomes unable to meet the real-time solution requirement when the aggregation size reaches six buildings. In contrast, distributed MPC (DMPC) remains real-time feasible throughout the tested range and shows only mild solution-time growth, accompanied primarily by increasing Tracking ADMM iterations as aggregation size grows. Where centralized references are available, finite-iteration DMPC approaches the centralized objective while satisfying the stopping tolerances. Coordination yields bill savings of up to 9.6% when sufficient photovoltaic surplus and consumer demand coincide, whereas low-surplus savings can be masked by closed-loop numerical variation associated with finite solver convergence tolerances. The proposed feature sets reduce horizon-mean weighted standardized primary-target mean squared error by up to 9.7% and 33.8% for the office and apartment testbeds, respectively.

arXiv comment20 pages, 5 figures, 5 tables. Revised manuscript with substantial updates to the methodology, experiments, results, and discussion