Volume 68

A Multi-Objective HVAC Control Framework for Net-Zero-Oriented PV-Integrated Residential Modular Building Thi Ngoc Yen Huynh, Anh Tuan Nguyen, Theodore Kabangu Nkashama, Yong Han Ahn

Abstract

Residential modular buildings require Heating, Ventilation, and Air Conditioning (HVAC) control strategies that can be deployed rapidly while maintaining reliable performance under dynamic operating conditions. This need is intensified by rising cooling demand, stricter Indoor Air Quality (IAQ) requirements, and growing interest in on-site Photovoltaic (PV) systems for net-zero-oriented operation. This study proposes a plug-and-play multi-objective HVAC control framework for PV-integrated residential modular buildings based on a Multi-Objective Markov Decision Process (MOMDP) and Envelope Q-Learning (EQL). A residential modular case study is evaluated in whole-year simulation. EQL outperformed scalarized Deep Reinforcement Learning (DRL) and Multi-Objective Model Predictive Control (MOMPC) across all tested preference settings. Under the balanced preference, annual HVAC grid import was reduced to 261 kWh, versus 767 kWh for Scalarized DRL and 1028 kWh for MOMPC. Under the energy preference, grid import fell further to 150 kWh. In the balanced case, EQL also improved cumulative energy, comfort, and IAQ rewards by 61.4%, 58.6%, and 21.1% over Scalarized DRL, and by 71.6%, 81.0%, and 23.0% over MOMPC. These results show strong potential for robust, scalable, and net-zero-oriented HVAC control in PV-integrated modular buildings.

Keywords HVAC control, residential modular buildings, multi-objective reinforcement learning, photovoltaic integration, net-zero operation