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Predictive Modelling of Stack Effect Using Machine Learning Algorithms in High-Rise Office Buildings

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posted on 2024-02-22, 14:47 authored by Gary Chang

Stack effect adversely affects heating loads in high-rise buildings due to excess infiltration caused by pressure differentials. Mitigating the effects of this phenomenon is crucial to meet the increasingly ambitious energy targets such as Canada’s commitment to COP21. A novel datadriven modeling approach is implemented in order to classify instances where stack effect is detrimental to building performance based on the pressure differentials, building automation system (BAS), and weather data. Various machine learning algorithms were tested with K-nearest neighbor (KNN) achieving the best results for stack effect detection via pressure differentials with an accuracy of 97.7%. The research also investigated potential methods of simplifying stack effect detection by using BAS data as a proxy variable with the best model being able to correctly classify 77.5% of the instances during the cooling season.

History

Language

eng

Degree

  • Master of Applied Science

Program

  • Building Science

Granting Institution

Ryerson University

LAC Thesis Type

  • Thesis

Thesis Advisor

Jenn McArthur

Year

2021

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    Building Science (Theses)

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