1. Data-driven HVAC Control Using Symbolic Regression: Design and Implementation
- Author
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Ozawa, Yuki, Zhao, Dafang, Watari, Daichi, Taniguchi, Ittetsu, Suzuki, Toshihiro, Shimoda, Yoshiyuki, and Onoye, Takao
- Subjects
FOS: Computer and information sciences ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,FOS: Electrical engineering, electronic engineering, information engineering ,Systems and Control (eess.SY) ,Electrical Engineering and Systems Science - Systems and Control - Abstract
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., 5 pages with 6 figures, accepted by IEEE PES GM 2023 (23PESGM0543) This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
- Published
- 2023