Building and Urban Data Science (BUDS) Lab

About the lab

BUDS Lab is a scientific research group that leverages data sources from the built and urban environments to improve the energy efficiency and conservation, comfort, safety and satisfaction of humans.

Featured research (43)

This work presents a study on the characterization of the air-conditioning (AC) usage pattern of non-residential buildings from thermal images collected from an urban-scale infrared (IR) observatory. To achieve this first, an image processing scheme, for cleaning and extraction of the temperature time series from the thermal images is implemented. To test the accuracy of the thermal measurements using IR camera, the extracted temperature is compared against the ground truth surface temperature measurements. It is observed that the detrended thermal measurements match well with the ground truth surface temperature measurements. Subsequently, the operational pattern of the water-cooled systems and window AC units are extracted from the analysis of the thermal signature. It is observed that for the water-cooled system, the difference between the rate of change of the window and wall can be used to extract the operational pattern. While, in the case of the window AC units, wavelet transform of the AC unit temperature is used to extract the frequency and time domain information of the AC unit operation. The results of the analysis are compared against the indoor temperature sensors installed in the office spaces of the building. It is realized that the accuracy in the prediction of the operational pattern is highest between 8 pm to 10 am, and it reduces during the day because of solar radiation and high daytime temperature. Subsequently, a characterization study is conducted for eight window/split AC units from the thermal image collected during the nighttime. This forms one of the first studies on the operational behavior of HVAC systems for non-residential buildings using the longitudinal thermal imaging technique. The output from this study can be used to better understand the operational and occupant behavior, without requiring to deploy a large array of sensors in the building space.

Lab head

Clayton Miller
  • Department of the Built Environment
About Clayton Miller
  • Dr. Clayton Miller is an Asst. Professor at NUS in the BUDS Lab, the Co-Leader of Theme D - Data Analytics at the UC Berkeley SinBerBEST2 Lab and the Co-Leader of Subtask 4 of the IEA Annex 79 Occupant-Centric Building Design and Operation. He holds a Doctor of Sciences (Dr. sc. ETH Zurich) from the ETH Zürich, an MSc. (Building) from the National University of Singapore (NUS), and a BSc./Masters of Architectural Engineering (MAE) from the University of Nebraska - Lincoln (UNL).

Members (9)

Matias Quintana
  • National University of Singapore
Mario Frei
  • National University of Singapore
Martín Mosteiro Romero
  • National University of Singapore
Chun Fu
  • National University of Singapore
Vasantha Ramani
  • Berkeley Education Alliance for Research in Singapore
Yun Xuan Chua
  • National University of Singapore
Charlene Tan
  • National University of Singapore
Charis Boey
  • National University of Singapore

Alumni (11)

Chuan Fu Tan
  • Johnson Controls
Pandarasamy Arjunan
  • Berkeley Education Alliance for Research in Singapore (BEARS) Limited
Prageeth Jayathissa
  • Vector Limited