skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage Approach
Title: An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage Approach
Heat loss quantification (HLQ) is an essential step in improving a building’s thermal performance and optimizing its energy usage. While this problem is well-studied in the literature, most of the existing studies are either qualitative or minimally driven quantitative studies that rely on localized building envelope points and are, thus, not suitable for automated solutions in energy audit applications. This research work is an attempt to fill this gap of knowledge by utilizing intensive thermal data (on the order of 100,000 plus images) and constitutes a relatively new area of analysis in energy audit applications. Specifically, we demonstrate a novel process using deep-learning methods to segment more than 100,000 thermal images collected from an unmanned aerial system (UAS). To quantify the heat loss for a building envelope, multiple stages of computations need to be performed: object detection (using Mask-RCNN/Faster R-CNN), estimating the surface temperature (using two clustering methods), and finally calculating the overall heat transfer coefficient (e.g., the U-value). The proposed model was applied to eleven academic campuses across the state of North Dakota. The preliminary findings indicate that Mask R-CNN outperformed other instance segmentation models with an mIOU of 73% for facades, 55% for windows, 67% for roofs, 24% for doors, and 11% for HVACs. Two clustering methods, namely K-means and threshold-based clustering (TBC), were deployed to estimate surface temperatures with TBC providing consistent estimates across all times of the day over K-means. Our analysis demonstrated that thermal efficiency not only depended on the accurate acquisition of thermal images but also relied on other factors, such as the building geometry and seasonal weather parameters, such as the outside/inside building temperatures, wind, time of day, and indoor heating/cooling conditions. Finally, the resultant U-values of various building envelopes were compared with recommendations from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards.  more » « less
Award ID(s):
1920011
PAR ID:
10297574
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Sensors
Volume:
21
Issue:
13
ISSN:
1424-8220
Page Range / eLocation ID:
4375
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, Journal of Energy Resources Technology)
    Abstract One of the major challenges in the development of micro-combustors is heat losses that result in flame quenching, and reduced combustion efficiency and performance. In this work, a novel thermal barrier coating (TBC) using hexagonal boron nitride (h-BN) nanosheets as building blocks was developed and applied to a Swiss roll micro-combustor for determining its heat losses with increased temperatures inside the combustor that contributes to improved performance. It was found that by using the h-BN TBC, the combustion temperature of the micro-combustor increased from 850 K to 970 K under the same thermal loading and operational conditions. This remarkable temperature increase using the BN TBC originated from its low cross-plane thermal conductivity of 0.4 W m−1 K−1to mitigate the heat loss from the micro-combustor plates. Such a low thermal conductivity in the h-BN TBC is attributed to its interfacial resistance between the nanosheets. The development of h-BN TBC provides an effective approach to improve thermal management for performance improvements of gas turbine engines, rocket engines, and all various kinds of micro-combustors. 
    more » « less
  2. ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    In this paper, we look at how depth data can benefit existing object masking methods applied in occluded scenes. Masking the pixel locations of objects within scenes helps computers get a spatial awareness of where objects are within images. The current state-of-the-art algorithm for masking objects in images is Mask R-CNN, which builds on the Faster R-CNN network to mask object pixels rather than just detecting their bounding boxes. This paper examines the weaknesses Mask R-CNN has in masking people when they are occluded in a frame. It then looks at how depth data gathered from an RGB-D sensor can be used. We provide a case study to show how simply applying thresholding methods on the depth information can aid in distinguishing occluded persons. The intention of our research is to examine how features from depth data can benefit object pixel masking methods in an explainable manner, especially in complex scenes with multiple objects. 
    more » « less
  3. ; ; (, 2019 IEEE International Conference on Smart Computing (SMARTCOMP))
    Proper thermal insulation yields optimum energy expenses in buildings by maintaining necessary heat gain or loss through the built envelope. However, improper thermal insulation causes significant energy wastage along with infusing various damages on indoor and outdoor walls of the buildings, for example, damp areas, black stains, cracks, paint bubbles etc. Therefore, it is important to inspect the temperature variations in different areas of the built environments in regular basis. We propose a method for identifying temperature variance in building thermal images based on Symbolic Aggregated Approximation (SAX). Our process helps detect the temperature variation over different image segments and infers the fault prone segments of leakages. We have collected about 50 thermal images associated with different types of wall specific insulation problems in indoor built environment and were able to identify the affected area with approximately 75% accuracy using our proposed method based on temperature variation detection approach. 
    more » « less
  4. ; ; ; ; ; ; (, Solar Energy)
    A Photothermal Solar Tunnel Radiator (PSTR) is designed and developed by employing multiple transparent photothermal glass panels (TPGP). The primary objective is to pioneer a transformative approach to achieve energy-neutral building heating utilities, exemplified by a lab-scale "Photothermal Solar Box" (PSB) exclusively heated with TPGP under natural sunlight. The PSTR presents a novel paradigm for sustainable energy, enabling direct solar energy capture through transparent glass substrates with photothermal coatings. The high transparency of Fe3O4@Cu2-xS coated glass substrates enhance efficient solar harvesting and photothermal energy generation within the Photothermal Solar Box. The system demonstrates an impressive thermal energy output, reaching up to 9.1x105 joules with 8 photothermal panels in parallel. Even under colder conditions (ambient temperature: -10 °C), with accelerated heat loss, the interior temperatures of the PSB with partial thermal insulation achieve a commendable 35 °C, showcasing effective photothermal heating in cold weather. These findings indicate the system's resilience and efficiency in harnessing solar energy under diverse conditions, including partial cloudy weather. The initiative contributes to broader sustainability goals by providing a scalable and practical alternative to traditional solar heating methods, aligning with the global mission for a cleaner, greener future. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al (, Advanced Materials)
    Abstract Van der Waals interactions in 2D materials have enabled the realization of nanoelectronics with high‐density vertical integration. Yet, poor energy transport through such 2D–2D and 2D–3D interfaces can limit a device's performance due to overheating. One long‐standing question in the field is how different encapsulating layers (e.g., contact metals or gate oxides) contribute to the thermal transport at the interface of 2D materials with their 3D substrates. Here, a novel self‐heating/self‐sensing electrical thermometry platform is developed based on atomically thin, metallic Ti3C2MXene sheets, which enables experimental investigation of the thermal transport at a Ti3C2/SiO2interface, with and without an aluminum oxide (AlOx) encapsulating layer. It is found that at room temperature, the thermal boundary conductance (TBC) increases from 10.8 to 19.5 MW m−2K−1upon AlOxencapsulation. Boltzmann transport modeling reveals that the TBC can be understood as a series combination of an external resistance between the MXene and the substrate, due to the coupling of low‐frequency flexural acoustic (ZA) phonons to substrate modes, and an internal resistance between ZA and in‐plane phonon modes. It is revealed that internal resistance is a bottle‐neck to heat removal and that encapsulation speeds up the heat transfer into low‐frequency ZA modes and reduces their depopulation, thus increasing the effective TBC. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email