Operational characteristics of residential air conditioners with temporally granular remote thermographic imaging
Collecting accurate information about the operational characteristics of buildings is essential for many performance analyses including energy auditing and benchmarking. While most of the existing solutions involve manual inspection of building premises, surveys, or in-situ sensor deployment, these techniques are intrusive and/or time-intensive, limiting their applicability at larger, city-level spatial scales. Complementing these solutions, we present a wide-area infrared thermography (IRT) based system for measuring a building’s operational characteristics remotely in a non-intrusive and scalable way, and we describe the image capture, processing pipeline, and preliminary results from an initial deployment of this system focusing on the operational characteristics of air-conditioning units. The data collection consisted of infrared imaging of a residential building for 50 days at continuous 10-second intervals. The infrared brightness variations of exterior split air-conditioners fixtures were extracted for each image, and operational attributes were then extracted from the resultant time series. Using state-based change point detection methods to determine times at which air-conditioners are operational, our preliminary analysis focuses on phenomenological patterns of activity with two main findings. First, we demonstrate our ability to determine the operational characteristics of air-conditioners and in particular the fact that they exhibit two distinct modes: continuous operation and cycling behavior. Second, we find that the fraction of “on” time spent in the cycling mode is characteristically longer for lower external temperatures, consistent with the hypothesis that the cycling mode represents a reaching of set temperature. Finally, we outline future research directions and challenges in leveraging IRT for behavioral studies of cooling system use and proactive assessment of air-conditioners towards the development of scalable virtual energy auditing.
Team: Pandarasamy Arjunan, Gregory Dobler (University of Delaware, USA), Kyungmin Lee (University of Delaware, USA), Filip Biljecki (NUS), Clayton Miller (NUS), and Kameshwar Poolla (UC Berkeley)
Longitudinal thermal imaging for scalable non-residential HVAC and occupant behaviour characterization
This work presents a study on the characterization of the air-conditioning (AC) usage pattern of non-residential buildings from longitudinal thermal images collected at the urban scale. The operational pattern of two different air-conditioning systems (water-cooled systems operating on a pre-set schedule and window AC units operated by the occupants) are studied from the thermal images. It is observed that for the water-cooled system, the difference between the rate of change of the window and wall temperature 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. This forms one of the first few 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.
Team: Vasantha Ramani (BEARS), Miguel Martin (BEARS), Pandarasamy Arjunan (BEARS), Adrian Chong (NUS), Kameshwar Poolla (UC Berkeley), and Clayton Miller (NUS)
- Energy and Buildings IF: 4.867Longitudinal thermal imaging for scalable non-residential HVAC and occupant behaviour characterizationEnergy and Buildings 2023