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Paige Wenbin Tien
Researcher at University of Nottingham
Publications - 24
Citations - 337
Paige Wenbin Tien is an academic researcher from University of Nottingham. The author has contributed to research in topics: HVAC & Computer science. The author has an hindex of 5, co-authored 14 publications receiving 104 citations.
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Journal ArticleDOI
Numerical and experimental investigation of the indoor air quality and thermal comfort performance of a low energy cooling windcatcher with heat pipes and extended surfaces
TL;DR: In this article, the authors investigated the thermal comfort and indoor air quality in buildings ventilated with a passive cooling windcatcher integrated with heat pipes and extended surface using numerical modelling, wind tunnel and far-field testing in the UAE during a summer month.
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A vision-based deep learning approach for the detection and prediction of occupancy heat emissions for demand-driven control solutions
TL;DR: The results showed that the occupancy heat gains could be represented more accurately in comparison to using static office occupancy profiles, and the accurate detection of occupants and their activities can also be used to effectively estimate CO2 concentration.
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Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method
TL;DR: A deep learning-based approach is developed which enables the detection and recognition of equipment usage and the associated heat emissions in office spaces and indicates that it is feasible to use the deep learning approach to predict equipment heat emission for achieving effective building energy management therefore to reduce building energy demand.
Journal ArticleDOI
Development of a natural ventilation windcatcher with passive heat recovery wheel for mild-cold climates: CFD and experimental analysis
John Kaiser Calautit,Dominic O’Connor,Paige Wenbin Tien,Shuangyu Wei,Conrad Allan Jay Pantua,Conrad Allan Jay Pantua,Ben Richard Hughes +6 more
TL;DR: In this article, a rotary heat recovery device suitable to be incorporated with a roof mounted multi-directional windcatcher system was developed and tested in a crossflow channel to assess the concept and performance of the design.
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A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand
TL;DR: A vision-based deep learning framework for the detection and recognition of manual window operation in buildings and a trained deep learning model is deployed into an artificial intelligence-powered camera to assess the proposed strategy's capabilities.