Application of model predictive control to building design optimisation

Abstract

Optimisation algorithms offer a valuable tool for enhancing the energy efficiency of buildings by fine-tuning specific design parameters. Model Predictive Control (MPC) emerges as a compelling technology to address the rising need for improved efficiency and adaptable operation within building energy systems. A simulation study using MATLAB and EnergyPlus was conducted to examine the influence of MPC and building design optimisation (BDO) on wall insulation thickness and internal mass area. The focus was on their impact on heating energy use and indoor thermal comfort in an office room located in Brussels, Belgium. The study revealed that the sole implementation of MPC led to a 7.6% decrease in heating energy use, while the application of BDO resulted in a more significant reduction of 12.8%. Remarkably, the fusion of MPC and BDO yielded the highest energy savings, cutting heating electricity usage by 23.4% compared to the baseline model. Moreover, MPC effectively maintained indoor temperature within the desired thermal comfort boundaries. The optimal wall insulation thickness and internal mass area were also ascertained through BDO, both of which exceeded the levels set by the baseline model. BDO, in conjunction with MPC, demanded the maximum permissible insulation thickness of 320 mm for the external north and south walls. Interestingly, when BDO was combined with MPC, the requirement for the internal mass area reduced by 11.7 m2 compared to utilising BDO alone. The study’s results underscore the potential of integrating MPC with BDO to elevate building energy efficiency. Furthermore, this strategy may be adaptable to optimising other building parameters at the early design stage, thereby augmenting overall building energy efficiency.