Longitudinal thermal imaging for scalable non-residential HVAC and
occupant behaviour characterization
- URL: http://arxiv.org/abs/2211.09288v3
- Date: Mon, 20 Mar 2023 08:24:20 GMT
- Title: Longitudinal thermal imaging for scalable non-residential HVAC and
occupant behaviour characterization
- Authors: Vasantha Ramani, Miguel Martin, Pandarasamy Arjunan, Adrian Chong,
Kameshwar Poolla, Clayton Miller
- Abstract summary: 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.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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