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Activity Number:
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340
- SPEED: SPAAC SESSION III
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Type:
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Topic-Contributed
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Date/Time:
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Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #318740
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Title:
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Temperature Clusters in Commercial Buildings Using K-Means and Time Series Clustering
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Author(s):
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Saman Muthukumarana and Matt Schaubroeck and Dan Loewen and Ashani Nuwanthika Wickramasinghe*
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Companies:
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University of Manitoba and ioAirFlow and ioAirFlow and University of Manitoba
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Keywords:
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k-means clustering;
Time series clustering;
Thermostats;
Building Environment Analysis;
Machine Learning
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Abstract:
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An efficient building should be able to control its internal temperature by considering both the building’s energy efficiency and comfort level of its occupants. Thermostats help to control the temperature within a building, and proper thermostat placement helps to better control a building’s temperature. More thermostats will provide a better understanding of a building’s temperature distribution. In order to determine the minimum number of thermostats required to accurately measure the internal temperature distribution of a building, it is necessary to find the locations that show similar environmental conditions. In this paper, we analyzed high resolution temperature measurements from a commercial building to assess the performance and health of the building. Then we conducted two cluster analyses to evaluate the efficiency of the existing zoning structure and to find the optimal number of clusters. K-means and time series clustering were used to identify the temperature clusters per building floor. Based on statistical assessments, we observed that time series clustering showed better results than k-means clustering.
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Authors who are presenting talks have a * after their name.
