Distributed Model Predictive Control for Building Automation systems: a parallel ADMM approach

This paper proposes a distributed Model Predictive Control (MPC)-based approach for comfort temperature tracking and electric consumption minimization in building automation systems (BASs). The developed optimisation model and overall architecture were designed with real-world applications in mind, incorporating in-field controllers and sensors. A distributed optimization algorithm is here proposed, which extends the well-known alternating direction method of multipliers (ADMM) to handle inequality constraints (that are necessary to model the typical local temperature sensors and actuators in smart buildings). The methodology is validated through testing on a real case study, namely the Smart Energy Building (SEB) at the Savona Campus of the University of Genoa, which is characterised by a geothermal heat pump, photovoltaics, storage systems, and charging stations. The algorithm enables reaching a comfortable temperature, limits power variation for the heat pump, and minimises costs. Regarding other solution methods, comparison with state-of-the-art approaches demonstrates a 25% reduction in the number of iterations needed for convergence