Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort

Abstract

Efficient HVAC devices are not sufficient to achieve high levels of building energy performance, since the regulation/control strategy plays a fundamental role. This study proposes a simulation-based model predictive control (MPC) procedure, consisting of the multi-objective optimization of operating cost for space conditioning and thermal comfort. The procedure combines EnergyPlus and MATLAB®, in which a genetic algorithm is implemented. The aim is to optimize the hourly set point temperatures with a day-ahead planning horizon, based on forecasts of weather conditions and occupancy profiles. The outcome is the Pareto front, and thus the set of non-dominated solutions, among which the user can choose according to his comfort needs and economic constraints. The critical issue of huge computational time, typical of simulation-based MPC, is overcome by adopting a reliable minimum run period. The procedure can be integrated in building automation systems for achieving a real-time optimized MPC. The methodology is applied to a multi-zone residential building located in the Italian city of Naples, considering a typical day of the heating season. Compared to a standard control strategy, the proposed MPC generates a reduction of operating cost up to 56%, as well as an improvement of thermal comfort.