Data-driven HVAC Control Using Symbolic Regression: Design and Implementation

TL;DR

This paper presents a data-driven HVAC control framework using symbolic regression models to optimize energy efficiency and thermal comfort, demonstrated through real-world campus building implementation.

Contribution

It introduces a novel methodology combining symbolic regression and model predictive control for HVAC systems, improving energy savings and peak power reduction.

Findings

Peak power reduced by 16.1% compared to traditional thermostats.

Effective modeling of building thermodynamics using symbolic regression.

Demonstrated real-world applicability on campus building data.

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.