Biography
Dr Zeynep Duygu Tekler is a Postdoctoral Researcher in the Department of Engineering Science at the University of Oxford. Her research focuses on occupant-centric smart building technologies and machine learning approaches for high-performance buildings. Her postdoctoral work, sponsored by the EPSRC/UKRI grant, involves developing scalable and cost-effective building technologies and implementing data-driven approaches for collecting and analyzing energy data at scale as part of the UK EDOL Programme.
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 top-tier peer-reviewed journals, and she actively serves as a reviewer for journals including Building and Environment, Energy and Buildings, and Applied Energy. She was the recipient of the prestigious SINGA PhD scholarship and was awarded the SUTD Best Research Project Award for her patented work 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.
Research Groups
Related Academics
Publications
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"
}
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"
}
Occupancy prediction using deep learning approaches across multiple space types: A minimum sensing strategy
Tekler ZD & Chong A (2022), Building and Environment, 226, 109689
BibTeX
@article{occupancypredic-2022/12,
title={Occupancy prediction using deep learning approaches across multiple space types: A minimum sensing strategy},
author={Tekler ZD & Chong A},
journal={Building and Environment},
volume={226},
pages={109689},
publisher={Elsevier},
year = "2022"
}
ROBOD, room-level occupancy and building operation dataset
Tekler ZD, Ono E, Peng Y, Zhan S, Lasternas B et al. (2022), Building Simulation, 15(12), 2127-2137
BibTeX
@article{robodroomlevelo-2022/12,
title={ROBOD, room-level occupancy and building operation dataset},
author={Tekler ZD, Ono E, Peng Y, Zhan S, Lasternas B et al.},
journal={Building Simulation},
volume={15},
pages={2127-2137},
publisher={Springer Nature},
year = "2022"
}
Hybrid system controls of natural ventilation and HVAC in mixed-mode buildings: A comprehensive review
Peng Y, Lei Y, Tekler ZD, Antanuri N, Lau S-K et al. (2022), Energy and Buildings, 276, 112509
BibTeX
@article{hybridsystemcon-2022/12,
title={Hybrid system controls of natural ventilation and HVAC in mixed-mode buildings: A comprehensive review},
author={Peng Y, Lei Y, Tekler ZD, Antanuri N, Lau S-K et al.},
journal={Energy and Buildings},
volume={276},
pages={112509},
publisher={Elsevier},
year = "2022"
}
Plug-Mate: An IoT-based occupancy-driven plug load management system in smart buildings
Tekler ZD, Low R, Yuen C & Blessing L (2022), Building and Environment, 223, 109472
BibTeX
@article{plugmateaniotba-2022/9,
title={Plug-Mate: An IoT-based occupancy-driven plug load management system in smart buildings},
author={Tekler ZD, Low R, Yuen C & Blessing L},
journal={Building and Environment},
volume={223},
pages={109472},
publisher={Elsevier},
year = "2022"
}
User perceptions on the adoption of smart energy management systems in the workplace: Design and policy implications
Tekler ZD, Low R & Blessing L (2022), Energy Research & Social Science, 88, 102505
BibTeX
@article{userperceptions-2022/6,
title={User perceptions on the adoption of smart energy management systems in the workplace: Design and policy implications},
author={Tekler ZD, Low R & Blessing L},
journal={Energy Research & Social Science},
volume={88},
pages={102505},
publisher={Elsevier},
year = "2022"
}
Near-real-time plug load identification using low-frequency power data in office spaces: Experiments and applications
Tekler ZD, Low R, Zhou Y, Yuen C, Blessing L et al. (2020), Applied Energy, 275, 115391
BibTeX
@article{nearrealtimeplu-2020/10,
title={Near-real-time plug load identification using low-frequency power data in office spaces: Experiments and applications},
author={Tekler ZD, Low R, Zhou Y, Yuen C, Blessing L et al.},
journal={Applied Energy},
volume={275},
pages={115391},
publisher={Elsevier},
year = "2020"
}
Predicting Commercial Vehicle Parking Duration using Generative Adversarial Multiple Imputation Networks
Low R, Tekler ZD & Cheah L (2020), Transportation Research Record Journal of the Transportation Research Board, 2674(9), 820-831
BibTeX
@article{predictingcomme-2020/9,
title={Predicting Commercial Vehicle Parking Duration using Generative Adversarial Multiple Imputation Networks},
author={Low R, Tekler ZD & Cheah L},
journal={Transportation Research Record Journal of the Transportation Research Board},
volume={2674},
pages={820-831},
publisher={SAGE Publications},
year = "2020"
}
A scalable Bluetooth Low Energy approach to identify occupancy patterns and profiles in office spaces
Tekler ZD, Low R, Gunay B, Andersen RK & Blessing L (2020), Building and Environment, 171, 106681
BibTeX
@article{ascalableblueto-2020/3,
title={A scalable Bluetooth Low Energy approach to identify occupancy patterns and profiles in office spaces},
author={Tekler ZD, Low R, Gunay B, Andersen RK & Blessing L},
journal={Building and Environment},
volume={171},
pages={106681},
publisher={Elsevier},
year = "2020"
}
