The large amount of data collected in buildings makes energy management smarter and more energy efficient. This study proposes a design and implementation methodology of data-driven heating, ventilation, and air conditioning (HVAC) control. Building thermodynamics is modeled using a symbolic regression model (SRM) built from the collected data. Additionally, an HVAC system model is also developed with a data-driven approach. A model predictive control (MPC) based HVAC scheduling is formulated with the developed models to minimize energy consumption and peak power demand and maximize thermal comfort. The performance of the proposed framework is demonstrated in the workspace in the actual campus building. The HVAC system using the proposed framework reduces the peak power by 16.1\% compared to the widely used thermostat controller.

翻译：建筑中收集的大量数据使能源管理更加智能化、节能化。本研究提出一种数据驱动型供暖、通风和空调（HVAC）控制的设计与实现方法。利用采集数据构建符号回归模型（SRM）对建筑热力学进行建模，同时采用数据驱动方法开发暖通空调系统模型。基于所建模型制定模型预测控制（MPC）的暖通空调调度方案，以最小化能耗与峰值功率需求并最大化热舒适度。在真实校园建筑的工作空间中验证了所提出框架的性能。与广泛使用的恒温控制器相比，采用该框架的暖通空调系统将峰值功率降低了16.1%。