This work investigates a coupling-aware, decentralized formulation of the on-policy SARSA (State–Action–Reward–State–Action) algorithm for real-time HVAC control in multi-zone open-plan offices and indicates that the approach is computationally compatible with resource-constrained building energy management system (BEMS) hardware, although embedded field validation remains future work.
HVAC systems represent a major share of building energy consumption. Traditional control strategies are limited in coordinating energy-comfort tradeoffs across multiple zones simultaneously. Reinforcement learning (RL) offers adaptive, data-driven control that optimizes performance over time. However, deploying learned...
Oussama Ziadi, A. Rochd, Samir Idrissi Kaitouni et al.· Conference on Control Techno...· 0 citations
HVAC control trades energy against thermal comfort, complicated by two building features: thermal mass spreads a setpoint change over hours, and the input-to-outcome mapping shifts across the year. Model-free algorithms such as PPO, SAC, and TD3 carry no model of building dynamics and cannot evaluate a setpoint’s downs...
Heating, Ventilation, and Air Conditioning (HVAC) systems account for 60–70% of residential electricity consumption in Oman, where extreme desert climate, with temperatures regularly exceeding 45 °C create substantial cooling demands. Unlike general reviews of smart HVAC controls, this study specifically evaluates the...
Mohammed Abu Safaqah, J. Natarajan, Khalid Anwar· Buildings· 0 citations
A Smart Air Conditioning Management System based on a Deep Q-Network agent capable of dynamically balancing energy use and thermal comfort and demonstrates that reinforcement learning enables adaptive AC control, offering a scalable approach to energy-efficient building management.
Jason Harvey Lorenzo, Justin Kyle O. Ricafort, E. Q. Macabebe· IOP Conference Series: Earth...· 0 citations
To address the limitations of existing central air conditioning energy-saving algorithms—such as their inability to achieve conventional optimization or adapt to grid peak shaving, coupled with nonlinear system dynamics, environmental uncertainties, and high-dimensional optimization challenges—we propose an integrated...
Junjie Lin, Zhuo-Fu Deng· International Conference on...· 0 citations
Modern smart buildings face the challenge of balancing energy-saving requirements with strict indoor air quality regulations. The aim of this research is to develop an intelligent, energy-efficient, and robust multi-pollutant forecasting and control framework that integrates hybrid LSTM–GRU forecasting with Proximal Po...
Walaa N. Ismail, Mona A. S. Ali· Sustainability· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
