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Urban Building Energy Models for District Cooling: A Data-Driven Approach Considering Building and Occupant Behavior Dynamics

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Urban Building Energy Models for District Cooling: A Data-Driven Approach Considering Building and Occupant Behavior Dynamics

Ahmed, Omar (2024) Urban Building Energy Models for District Cooling: A Data-Driven Approach Considering Building and Occupant Behavior Dynamics. Masters thesis, Concordia University.

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Abstract

District cooling offers an energy-efficient solution for hot urban regions where cooling demands are high. Accurate and rapid predictions of cooling requirements are vital during the planning phase to support informed decision-making. Surrogate models, which combine physics-based simulations with statistical or machine learning techniques, can harness the strengths of both methods, leading to more accurate building energy predictions at a low computational cost. In this study, a surrogate model, which combines machine learning with building physics-based archetypes, is employed to predict the cooling energy use intensities for high-rise buildings in a mixed-use district. The proposed surrogate models predict the impact of building design parameters, building operation characteristics, and occupant-related parameters on building energy performance. High-rise building models, representative of the district, are created using EnergyPlus software. The detailed cooling load profiles of these baseline models are simulated, analyzed, and validated against measured data and literature benchmarks. The resulting cooling loads are then aggregated at the district level, providing a physics-based method for urban-scale energy prediction. Parametric simulations are automated in RStudio using the developed archetypes by altering key parameters such as building envelope characteristics, geometry, and operational parameters, including occupant behavior. The resulting datasets are used to train machine learning models to approximate the outcomes of physics-based simulations. Additionally, the trained models are integrated into a user-friendly interface, enabling computationally efficient predictions of cooling requirements for each building in the district. The developed models show excellent performance, with R² values near 1 and RMSE below 0.17 kWh/m²/month on unseen data. This study demonstrates the potential of surrogate machine learning in predicting and optimizing building energy performance under different design, operation, and occupancy settings. It also provides insights into the impact of training dataset size on the accuracy of surrogate machine learning models.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Ahmed, Omar
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Building Engineering
Date:September 2024
Thesis Supervisor(s):Wang, Liangzhu (Leon) and Hassan, Ibrahim Galal
Keywords:Key Words: Urban Building Energy Modeling, District Cooling, Surrogate Modeling, Machine Learning, Building Performance Analysis, Occupant Behavior.
ID Code:994679
Deposited By: Omar Ahmed
Deposited On:17 Jun 2025 17:09
Last Modified:17 Jun 2025 17:09

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