Published in Indoor Air 2026, 2026
As a pilot of a large-scale field campaign, we developed a smartwatch-based JITAI to nudge occupants toward IAQ-improving behaviors and evaluated it through a micro-randomized field trial in Singapore.
Published in BuildSys 2025, 2025
This poster abstract presents a pilot field deployment evaluating a body thermoregulation-based dynamic bedroom air temperature control strategy designed for typical Singapore homes.
Published in CISBAT 2025, 2025
This paper outlines the methodology and preliminary results of the field-based portion of a study on understanding and mitigating the impacts of urban heat on sleep and physical activity.
Published in Healthy Building Conference 2025, 2025
This study investigated bedroom ventilation and air quality in Singapore’s tropical climate. Comfort parameters along with multiple air pollutants were measured in seven bedrooms, each for approximately one month.
Published in Energy and Buildings, 2024
Employ text mining and Natural Language Processing (NLP) techniques to thoroughly examine the connections among these approaches in the context of human-building interaction and occupant context-aware support.
Recommended citation: Zhang, W., Quintana, M., & Miller, C. (2025). Recommender systems and reinforcement learning for human-building interaction and context aware support: A text mining-driven review of scientific literature. Energy and Buildings, 329, 115247. doi:10.1016/j.enbuild.2024.115247 https://www.sciencedirect.com/science/article/pii/S037877882401363X
Published in Journal of Building Engineering, 2024
Model-free and model-based reinforcement learning algorithms were implemented; Reinforcement learning of value-based, policy gradient and actor-critic are discussed.
Recommended citation: Wu, Z., Zhang, W., Tang, R., Wang, H., & Korolija, I. (2024). Reinforcement learning in building controls: A comparative study of algorithms considering model availability and policy representation. Journal of Building Engineering, 90, 109497. doi:10.1016/j.jobe.2024.109497 https://www.sciencedirect.com/science/article/pii/S2352710224010659?via%3Dihub
Published in IOP Conference Series Earth and Environmental Science, 2022
Developed an energy efficient operation strategy for VRF system during the cooling season for a case office and achieved energy savings of up to 16.1% as well as improved thermal comfort compared to a rule-based control strategy.
Recommended citation: Zhang, W., & Zhang, Z. (2022). Energy Efficient Operation Optimization of Building Air-conditioners via Simulator-assisted Asynchronous Reinforcement Learning. IOP Conference Series: Earth and Environmental Science, 1048(1), 012006. doi:10.1088/1755-1315/1048/1/012006 https://iopscience.iop.org/article/10.1088/1755-1315/1048/1/012006