Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Data-driven approach to prioritize residential buildings’ retrofits in cold climates using smart thermostat data

Data-driven approach to prioritize residential buildings’ retrofits in cold climates using smart... At least 65% of existing residential buildings will still be in use by 2050, thus retrofitting existing buildings is critical to reducing energy consumption. However, prioritizing building retrofits typically requires a thorough evaluation of their thermal performance, which can be cost-prohibitive, especially on a large scale. To this end, this study presents a data-driven framework to target buildings for retrofits using smart thermostat data. To validate the framework, it was applied to 60,000 homes across North America using four years of real-time measurements. First, grey-box modelling approaches were used to estimate the thermal time constant for each home. Homes were then clustered according to their estimated values and for each cluster, the priority of retrofit was ranked. Finally, a classification model was developed to predict the priority of retrofit. Using a large sample size, the results can be used to prioritize buildings for retrofits when limited information is available. HIGHLIGHTS Thermostat data from over 60,000 houses were used to estimate their thermal performance. Two grey-box methods to estimate a building's thermal time constant (RC value) were compared. The estimated time constant values were used to cluster houses based on thermal performance. A classification model was developed to prioritize retrofits for each house based on its attributes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Architectural Science Review Taylor & Francis

Data-driven approach to prioritize residential buildings’ retrofits in cold climates using smart thermostat data

Doma, Aya; Ouf, Mohamed
Architectural Science Review , Volume 66 (3): 15 – May 4, 2023
Download PDF
15 pages

Loading next page...
 
/lp/taylor-francis/data-driven-approach-to-prioritize-residential-buildings-retrofits-in-WV5pivBNO6

References (34)

Publisher
Taylor & Francis
Copyright
© 2023 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1758-9622
eISSN
0003-8628
DOI
10.1080/00038628.2023.2193164
Publisher site
See Article on Publisher Site

Abstract

At least 65% of existing residential buildings will still be in use by 2050, thus retrofitting existing buildings is critical to reducing energy consumption. However, prioritizing building retrofits typically requires a thorough evaluation of their thermal performance, which can be cost-prohibitive, especially on a large scale. To this end, this study presents a data-driven framework to target buildings for retrofits using smart thermostat data. To validate the framework, it was applied to 60,000 homes across North America using four years of real-time measurements. First, grey-box modelling approaches were used to estimate the thermal time constant for each home. Homes were then clustered according to their estimated values and for each cluster, the priority of retrofit was ranked. Finally, a classification model was developed to predict the priority of retrofit. Using a large sample size, the results can be used to prioritize buildings for retrofits when limited information is available. HIGHLIGHTS Thermostat data from over 60,000 houses were used to estimate their thermal performance. Two grey-box methods to estimate a building's thermal time constant (RC value) were compared. The estimated time constant values were used to cluster houses based on thermal performance. A classification model was developed to prioritize retrofits for each house based on its attributes.

Journal

Architectural Science Review – Taylor & Francis

Published: May 4, 2023

Keywords: Retrofit; Residential buildings; thermal performance; Classification Model; Data-Driven grey-box models; Real-time measurements; smart thermostat

There are no references for this article.