Biography
Dr Zeynep Duygu Tekler is a Senior Research Associate in the Department of Engineering Science, and Junior Research Fellow at Kellogg College, University of Oxford. Her research focuses on occupant-centric smart building technologies and machine learning approaches for high-performance buildings. Her postdoctoral work involves developing scalable building technologies and data-driven approaches for analysing building energy consumption and occupant behavior in the built environment.
Before joining Oxford, she held a Postdoctoral Research Fellow position in the Department of the Built Environment at the National University of Singapore. She earned her PhD in Engineering Product Development at the Singapore University of Technology and Design (SUTD) under the SUTD-MIT International Design Center in 2021. Prior to her PhD, she received both her MSc and BSc in Industrial Engineering from Isik University, where she received the honor of Valedictorian.
Dr Tekler’s research has been published in leading journals including Building and Environment, Energy and Buildings, and Applied Energy. She is a Fellow of the Higher Education Academy and recipient of the prestigious SINGA PhD scholarship. At Oxford, she was named first runner-up at the Oxford Energy Hackathon for Net Zero and awarded the Seal of Excellence for her Marie Curie Fellowship proposal. She received the SUTD Best Research Project Award for her patented work, Plug-Mate, on an innovative occupancy-driven energy management system. She was also the winner of the SUTD 3MT Competition and the Asia-Pacific finalist in 2020.
Research Interests
- Internet of Things
- Occupancy Detection
- Thermal Comfort
- Smart Energy Management Systems
- Machine Learning
Current Projects
- The EDOL is an £8.7 million UKRI-EPSRC funded program, dedicated to collecting detailed longitudinal data on household energy use and associated socio-technical variables within the UK. This five-year initiative is focused on establishing a national energy data platform, designed to facilitate the country's transition towards net-zero carbon emissions.
- The openBES is an open-source building energy simulation tool (openBES) to inform decision-making for cost-effective and low-carbon interventions. As part of Oxford’s EPSRC Impact Acceleration Account (IAA), it will expand its impact to educational, professional, and non-technical audiences.
Research Groups
Related Academics
Publications
Occupancy monitoring approaches: learnings from literature and current research
Topouzi M, Suliman A & Tekler Z (2025)
BibTeX
@inproceedings{occupancymonito-2025/4,
title={Occupancy monitoring approaches: learnings from literature and current research},
author={Topouzi M, Suliman A & Tekler Z},
booktitle={CIBSE IBPSA-England Technical Symposium 2025},
year = "2025"
}
Data-efficient comfort modeling: Active transfer learning for predicting personal thermal comfort using limited data
Tekler ZD, Lei Y & Chong A (2024), Energy and Buildings, 319, 114507
BibTeX
@article{dataefficientco-2024/9,
title={Data-efficient comfort modeling: Active transfer learning for predicting personal thermal comfort using limited data},
author={Tekler ZD, Lei Y & Chong A},
journal={Energy and Buildings},
volume={319},
pages={114507},
publisher={Elsevier},
year = "2024"
}
Evaluating the sensitivity and robustness of occupancy models for building energy simulation during design
Ono E, Tekler ZD, Lam KP, Jin Y, Yan D et al. (2024), Building and Environment, 261, 111739
BibTeX
@article{evaluatingthese-2024/8,
title={Evaluating the sensitivity and robustness of occupancy models for building energy simulation during design},
author={Ono E, Tekler ZD, Lam KP, Jin Y, Yan D et al.},
journal={Building and Environment},
volume={261},
pages={111739},
publisher={Elsevier},
year = "2024"
}
Evaluating different levels of information on the calibration of building energy simulation models
Cheng S, Tekler ZD, Jia H, Li W & Chong A (2024), Building Simulation, 17(4), 657-676
BibTeX
@article{evaluatingdiffe-2024/4,
title={Evaluating different levels of information on the calibration of building energy simulation models},
author={Cheng S, Tekler ZD, Jia H, Li W & Chong A},
journal={Building Simulation},
volume={17},
pages={657-676},
publisher={Springer Nature},
year = "2024"
}
Experimental evaluation of thermal adaptation and transient thermal comfort in a tropical mixed-mode ventilation context
Lei Y, Tekler ZD, Zhan S, Miller C & Chong A (2024), Building and Environment, 248, 111043
BibTeX
@article{experimentaleva-2024/1,
title={Experimental evaluation of thermal adaptation and transient thermal comfort in a tropical mixed-mode ventilation context},
author={Lei Y, Tekler ZD, Zhan S, Miller C & Chong A},
journal={Building and Environment},
volume={248},
pages={111043},
publisher={Elsevier},
year = "2024"
}
Understanding the Impact of Building Characteristics on the Heat Pump Energy Consumption: A Case Study in the United Kingdom
Perelli-Rocco S, Tekler ZD & Grunewald P (2024)
BibTeX
@misc{understandingth-2024/,
title={Understanding the Impact of Building Characteristics on the Heat Pump Energy Consumption: A Case Study in the United Kingdom},
author={Perelli-Rocco S, Tekler ZD & Grunewald P},
year = "2024"
}
Evaluating the Sensitivity and Robustness of Occupancy Models for Building Energy Simulation During Design
Ono E, Tekler ZD, Lam KP, Jin Y, Yan D et al. (2024)
BibTeX
@misc{evaluatingthese-2024/,
title={Evaluating the Sensitivity and Robustness of Occupancy Models for Building Energy Simulation During Design},
author={Ono E, Tekler ZD, Lam KP, Jin Y, Yan D et al.},
year = "2024"
}
Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls
Tekler ZD, Lei Y, Dai X & Chong A (2023), Journal of Physics Conference Series, 2600(13), 132004
BibTeX
@inproceedings{enhancingperson-2023/11,
title={Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls},
author={Tekler ZD, Lei Y, Dai X & Chong A},
pages={132004},
year = "2023"
}
Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning
Zhuang D, Gan VJL, Tekler ZD, Chong A, Tian S et al. (2023), Applied Energy, 338, 120936
BibTeX
@article{datadrivenpredi-2023/5,
title={Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning},
author={Zhuang D, Gan VJL, Tekler ZD, Chong A, Tian S et al.},
journal={Applied Energy},
volume={338},
pages={120936},
publisher={Elsevier},
year = "2023"
}
A hybrid active learning framework for personal thermal comfort models
Tekler ZD, Lei Y, Peng Y, Miller C & Chong A (2023), Building and Environment, 234
BibTeX
@article{ahybridactivele-2023/3,
title={A hybrid active learning framework for personal thermal comfort models},
author={Tekler ZD, Lei Y, Peng Y, Miller C & Chong A},
journal={Building and Environment},
volume={234},
number={110148},
publisher={Elsevier},
year = "2023"
}
