Cost-optimal building thermal design in presence of multi-objective model predictive control for energy systems

A novel methodology is proposed in order to support the cost-optimal design of building envelope’s thermal characteristics and space conditioning systems in presence of an enhanced simulation-based model predictive control (MPC) for heating and cooling operations. The cost-optimal solution is identified by running a main mono-objective genetic algorithm (GA) that allows to minimize the global cost for space conditioning over building lifecycle. Each solution investigated by the GA represents a building thermal design combined with the MPC of space conditioning systems. In order to define the MPC strategy, the main mono-objective GA launches two secondary bi-objective GAs that optimize the heating and cooling operations, respectively. These secondary GAs perform a Pareto optimization by minimizing operating cost for space conditioning and thermal discomfort. They provide the optimal values of hourly set point temperatures for heating and cooling systems, with a day-ahead planning horizon, by considering the forecasts of weather conditions and building use. The optimal control strategy is detected based on needs and wills of users, who set a minimum level of thermal comfort to be fulfilled. The three employed GAs are implemented by coupling MATLAB® (optimization engine) with EnergyPlus (building performance simulation tool). For testing purposes, the methodology is applied for the thermal design of a new multi-zone residential building located in Naples (Southern Italy). It produces potential savings of 35.4 kWh/m2a as for primary energy consumption, and of around 7’000 € as for global cost, by ensuring the same satisfying comfort level, compared to standard approaches for building thermal design and space conditioning systems’ control.