DOI: https://doi.org/10.61435/ijred.2025.61410

Multi-objective HVAC control using genetic programming for grid-responsive commercial buildings

School of Energy & Environment, Southeast University, Nanjing, China 211189, China

Received: 20 May 2025; Revised: 7 Sep 2025; Accepted: 4 Oct 2025; Available online: 19 Oct 2025; Published: 1 Nov 2025.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2025 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Commercial buildings are significant energy consumers, with their heating, ventilation, and air conditioning (HVAC) systems being major contributors. Optimizing these systems is crucial for energy conservation, yet advanced artificial intelligence methods like Deep Reinforcement Learning (DRL) often produce opaque black-box solutions. While post-hoc explanation methods can offer some insight, they are often inexact and fail to render the core decision logic fully transparent, hindering trust and practical implementation. This paper presents a novel approach using Genetic Programming (GP) to automatically design HVAC control strategies that are both highly effective and inherently understandable. The novelty of our framework lies in its direct evolution of interpretable, multi-objective control policies that holistically co-optimize energy efficiency, occupant thermal comfort, and integrated Demand Response (DR) for a complex multi-zone system a combination not extensively explored in prior GP-HVAC research. We applied this framework to manage the central air handling unit of a simulated multi-zone office building, enabling it to dynamically adjust key settings like air temperature and fan pressure. Rigorous testing in a validated EnergyPlus simulation environment showed that the GP-designed control policies reduced annual HVAC energy use by 40.9% compared to standard ASHRAE A2006 guidelines, 28.4% against the advanced ASHRAE G36 standard, and a notable 9.3% more than a state-of-the-art DRL controller. These substantial energy savings were achieved while maintaining excellent occupant thermal comfort for 98.8% of occupied hours. Furthermore, the GP controller demonstrated robust performance during Demand Response scenarios, achieving a 72.1% reduction in peak power draw. A key outcome is that these high-performing strategies are expressed in a transparent format allowing direct inspection and understanding. This research establishes Genetic Programming as a compelling method for creating intelligent HVAC controls that are not only efficient and grid-responsive but also transparent, fostering greater confidence in advanced building automation.
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Keywords: Genetic Programming; HVAC Control; Energy Efficiency; Transparent Control; Demand Reponses; Building Automation

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Section: Original Research Article
Language : EN
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