| DOI | Resolve DOI: https://doi.org/10.1145/3486611.3491119 |
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| Author | Search for: Huchuk, Brent1; Search for: Bahiraei, Farid1; Search for: Dutta, Saptak1 |
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| Affiliation | - National Research Council of Canada. Construction
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| Format | Text, Article |
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| Conference | BuildSys '21: The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, November 17-18, 2021, Coimbra Portugal |
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| Abstract | Small commercial buildings owners and utility managers often look for opportunities for energy and greenhouse gas emission savings through various energy efficiency approaches. However, in Canada, the small commercial buildings are currently underserved by energy conservation tools because of their dispersion and lower payback potential. Connected thermostats provide a low-cost solution to collect massive amounts of data from a portfolio of these buildings that can be used to improve the understanding of their energy use behaviors. Similar to their residential application counterparts, these thermostats can be overridden by building occupants and potentially hinder more advanced controls or coordination across a managed building portfolio. In this work, we investigated the temperature setpoint overrides from more than 620 thermostats across 250 small commercial buildings located in Ontario, Canada. In particular, we developed a global model to estimate the fraction of buildings in the portfolio that experienced overrides each hour. We were able to correctly predict the percentage of overridden buildings within 2%. Such a predictive model may help the portfolio manager to forecast future conditions in order to create more efficient energy saving and peak reduction programs without compromising the occupants' thermal comfort and organizational productivity. |
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| Publication date | 2021-11-17 |
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| Publisher | ACM |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 1b994908-5a58-456d-ae58-a861f1a1d3a0 |
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| Record created | 2023-01-09 |
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| Record modified | 2023-01-09 |
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