Publications
X Author: Zhe Wang
2021
Li, Han, Zhe Wang, Tianzhen Hong, Andrew Parker, and Monica Neukomm."Characterizing patterns and variability of building electric load profiles in time and frequency domains."Applied Energy
291 (2021) 116721. DOI
Wang, Zhe, Tianzhen Hong, Han Li, and Mary Ann Piette."Predicting city-scale daily electricity consumption using data-driven models."Advances in Applied Energy
2 (2021) 100025. DOI
Li, Han, Zhe Wang, and Tianzhen Hong."Occupant-Centric key performance indicators to inform building design and operations."Journal of Building Performance Simulation
(2021) 1 - 29. DOI
2020
Cho, Brian, Teresa Dayrit, Yuan Gao, Zhe Wang, Tianzhen Hong, Alex Sim, and Kesheng."Effective Missing Value Imputation Methods for Building Monitoring Data."IEEE BigData 2020
(2020). DOI
Hong, Tianzhen, Daniel Macumber, Han Li, Katherine Fleming, and Zhe Wang."Generation and representation of synthetic smart meter data."Building Simulation
13.6 (2020) 1205 - 1220. DOI
Chen, Bingqing, Ming Jin, Zhe Wang, Tianzhen Hong, and Mario Bergés."Towards Off-policy Evaluation as a Prerequisite for Real-world Reinforcement Learning in Building Control."BuildSys '20: The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationProceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities
(2020). DOI
Wang, Zhe, and Tianzhen Hong."Generating realistic building electrical load profiles through the Generative Adversarial Network (GAN)."Energy and Buildings
224 (2020) 110299. DOI
Wang, Zhe, and Tianzhen Hong."Reinforcement learning for building controls: The opportunities and challenges."Applied Energy
269 (2020) 115036. DOI
Wang, Zhe, Hui Zhang, Yingdong Hi, Maohui Luo, Ziwei Li, Tianzhen Hong, and Borong Lin."Revisiting individual and group differences in thermal comfort based on ASHRAE database."Energy and Buildings
219 (2020) 110017. DOI
Wang, Zhe, Jingyi Wang, Yueer He, Yanchen Liu, Borong Lin, and Tianzhen Hong."Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning."Journal of Building Engineering
29 (2020) 101120. DOI
Wang, Zhe, Tianzhen Hong, and Mary Ann Piette."Building thermal load prediction through shallow machine learning and deep learning."Applied Energy
263 (2020) 114683. DOI
Hong, Tianzhen, Zhe Wang, Xuan Luo, and Wanni Zhang."State-of-the-art on research and applications of machine learning in the building life cycle."Energy and Buildings
212 (2020) 109831. DOI
Wang, Zhe, and Tianzhen Hong."Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States."Renewable and Sustainable Energy Reviews
119 (2020) 109593. DOI
Hong, Tianzhen, Chien-Fei Chen, Zhe Wang, and Xiaojing Xu."Linking human-building interactions in shared offices with personality traits."Building and Environment
170 (2020) 106602. DOI
2019
Wang, Jingyi, Zhe Wang, Ding Zhou, and Kaiyu Sun."Key issues and novel optimization approaches of industrial waste heat recovery in district heating systems."Energy
188 (2019) 116005. DOI
Wang, Zhe, Tianzhen Hong, Mary Ann Piette, and Marco Pritoni."Inferring occupant counts from Wi-Fi data in buildings through machine learning."Building and Environment
158 (2019) 281 - 294. DOI
Wang, Zhe, Tianzhen Hong, and Mary Ann Piette."Predicting plug loads with occupant count data through a deep learning approach."Energy
181 (2019) 29 - 42. DOI
Wang, Zhe, Thomas Parkinson, Peixian Li, Borong Lin, and Tianzhen Hong."The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes."Building and Environment
151 (2019) 219 - 227. DOI
Wang, Zhe, Tianzhen Hong, and Mary Ann Piette."Data fusion in predicting internal heat gains for office buildings through a deep learning approach."Applied Energy
240 (2019) 386 - 398. DOI
Wang, Zhe, and Tianzhen Hong."Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States."Renewable and Sustainable Energy Reviews
(2019) 109593. DOI