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Model predictive control strategies for buildings with mixed-mode cooling

机译：具有混合模式冷却的建筑物的模型预测控制策略

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The paper presents model predictive control (MPC) strategies for buildings with mixed-mode cooling (window opening position, fan assist, and night cooling schedule) and demonstrates their potential performance bounds in terms of energy savings within thermal comfort constraints, in comparison with standard heuristic rules used in current practice. The study also identifies optimal control sequences coordinated with shading, for the control of solar gains. A transient, multi-zone building energy prediction model, with a coupled thermal and airflow network, is developed in MATLAB, and it is used within an offline MPC framework with Particle Swarm Optimization (embedded in GenOpt) as an optimizer. Simulations are performed for a period of six consecutive summer days with mixed-mode cooling strategies decided by the predictive controller, based on weather forecast and cooling load anticipation over a 24 h planning horizon. The results show that MPC can significantly reduce the cooling requirements compared to baseline night setback control while maintaining the operative temperature during the occupied period within acceptable limits. On the contrary, rule-based control strategies for the window opening position, based on simple heuristics for the outdoor conditions, create an increased risk of overcooling with lower thermal comfort acceptability.
机译：本文介绍了采用混合模式制冷（窗户开启位置，风扇辅助和夜间制冷时间表）的建筑物的模型预测控制（MPC）策略，并展示了与标准相比，其在节能舒适度范围内的节能潜力。当前实践中使用的启发式规则。该研究还确定了与阴影协调的最佳控制序列，以控制太阳增益。在MATLAB中开发了具有耦合的热力和气流网络的瞬态，多区域建筑能量预测模型，该模型在离线MPC框架中使用，并以粒子群优化（嵌入GenOpt）作为优化器。在连续24天的计划时间内，根据天气预报和预计的冷却负荷，使用预测控制器确定的混合模式冷却策略进行连续六个夏季的模拟。结果表明，与基线夜间倒退控制相比，MPC可以显着降低冷却需求，同时将占用期间的工作温度保持在可接受的范围内。相反，基于室外条件的简单启发法，基于规则的车窗打开位置控制策略会增加过冷的风险，同时降低热舒适性。

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