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Hotspot analysis can identify areas with a higher concentration of events compared to the expected number in a given random distribution of events. Hotspot analysis has been widely used in various research disciplines, such as public health, crime, and environmental quality-related research. In this study, we applied hotspot analysis and Local Moran’s I indices to pinpoint high and low-temperature clusters within commercial buildings. Our study demonstrates that hotspot analysis is applicable inside a building. Using the Moran's I statistic, we introduced a method to determine the optimal number of neighbors.
Additionally, random forests were employed to identify important placement features influencing hot/cold spots. The vacancy of the place and the presence of windows emerged as crucial factors in our application. In conclusion, our combined application of Moran’s I statistic and hotspot analysis effectively identifies hot and cold areas within a building, when the number of neighbors is accurately identified and there are no outliers or abnormal sensors.
Additional Authors and Speakers (not including you)
Saman Muthukumarana
University of Manitoba
Matt Schaubroeck
ioAirFlow
Date and Time
-
Language of Oral Presentation
English / Anglais
Language of Visual Aids
English / Anglais

Speaker

Edit Name Primary Affiliation
Ashani N. Wickramasinghe University of Manitoba