Energy and temperature management in buildings through Multi-Objective Model Predictive Control on a chip

Climate control in buildings involves multiple conflicting objectives, such as energy consumption and occupant comfort, which have to be considered simultaneously during the operation of the climate control system. In this work, the Multi-Objective Model Predictive Control (MOMPC) solution method is further developed through the multiparametric programming approach (mpMOMPC). The MOMPC optimal control problem is reformulated according to the $ϵ$-constraint method, and the $ϵ$ vector is treated as unknown parameters to generate the control law expressions offline. This reduces online calculations to point location followed by function evaluation, enabling the controller to be implemented through a chip or low-cost hardware. To demonstrate the potential and versatility of the developed mpMOMPC algorithm, three case studies are conducted. Numerical simulation results show that the extreme-value case is the same as the rule-based MPC case and the preference function case results in maximum energy reduction by 20.1% compared to the rule-based MPC case.