最近，模型预测控制（MPC）被研究用于建筑系统的优化运行。与传统的控制方案相比，MPC基于考虑可用信息的系统行为预测来决定控制输入。此外，它可以灵活地响应在构建环境中总是发生的不可预测的干扰。我们对一栋8层办公楼进行了一项试点研究，该办公楼受到内部负荷和电价变化的影响，以检验所开发的MPC算法的潜力和鲁棒性。目标厂房的厂房系统由一个空气压缩机组成- 冷却器、分层水热储能装置和带有三个热交换器和五个变速泵的风机盘管装置。系统动力学在EnergyPlus中建模。该模型与楼宇控制虚拟试验台（BCVTB）相结合，自动生成人工神经网络模型的学习数据，作为优化问题的替代模型。采用εDE-RJ方法求解优化问题。在冷却季节的一天内，开发的MPC被用于最小化运行成本。 预测时间范围和控制间隔分别为10小时和1小时，控制变量为泵的质量流量。因此，通过实施已开发的MPC算法，我们确认，与受到意外干扰的常规控制相比，每天的能量消耗减少了约60%。引用:2018年年度会议，德克萨斯州休斯顿，会议论文

Recently, the model predictive control (MPC) has been studied for an optimal operation of the building system. Compared to a conventional control scheme,the MPC decides control inputs based on a prediction of system behavior considering available information. Moreover, it can flexibly respond to unpredictabledisturbances that always occur in a built environment. We conducted a pilot study for an 8-story office building which is subject to a change of internal loadand electricity price to examine the potential and robustness of developed MPC algorithm. The plant system of target building consists of an air-cooledchiller, stratified water thermal energy storage, and fan coil unit with three heat exchangers and five variable speed pumps. The system dynamics was modeledin EnergyPlus. This model was combined with Building Control Virtual Test Bed (BCVTB) to automatically generate learning data for an artificialneural network model that was used as a surrogate model in the optimization problem. The epsilon-constrained differential evolution with random jumping(εDE-RJ) method was used to solve the optimization problem. For a single day in cooling season, developed MPC was applied to minimize the operationcost. The prediction time horizon and control interval were 10 h and 1 h, respectively and the control variables were the mass flow rates of pumps. As aresult, by implementing developed MPC algorithm, we confirmed that the daily energy use was reduced by approximately 60% compared to conventionalcontrol subject to unexpected disturbances.