Z
Zhe Wang
Researcher at Lawrence Berkeley National Laboratory
Publications - 79
Citations - 3394
Zhe Wang is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Thermal comfort & Computer science. The author has an hindex of 21, co-authored 64 publications receiving 1558 citations. Previous affiliations of Zhe Wang include University of California, Berkeley & Tsinghua University.
Papers
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Journal ArticleDOI
Individual difference in thermal comfort: A literature review
Zhe Wang,Richard de Dear,Maohui Luo,Borong Lin,Yingdong He,Yingdong He,Ali Ghahramani,Yingxin Zhu +7 more
TL;DR: In this article, the authors examined the magnitude and significance of individual differences in the preferred/neutral/comfort temperature through reviewing previous climate chamber and field studies, including sex, age and etc.
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Reinforcement learning for building controls: The opportunities and challenges
Zhe Wang,Tianzhen Hong +1 more
TL;DR: Reinforcement Learning (RL), as an emerging control technique, has attracted growing research interest and demonstrated its potential to enhance building performance while addressing some limitations of other advanced control techniques, such as model predictive control.
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A review of operating performance in green buildings: Energy use, indoor environmental quality and occupant satisfaction
TL;DR: In this article, the authors reviewed the published researches on post-occupancy performance of green buildings in terms of energy use, indoor environment quality (IEQ) and occupant satisfaction.
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State-of-the-art on research and applications of machine learning in the building life cycle
TL;DR: This study systematically surveyed how machine learning has been applied at different stages of building life cycle and can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings.
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Building thermal load prediction through shallow machine learning and deep learning
TL;DR: It was found XGBoost and LSTM provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day’s data for prediction.