| Summary: | Urbanization and the increasing demand for accommodation and activities have led to
significant energy consumption and CO2 emissions in building construction. This thesis investigates
urban building energy performance (BEP) through a multi-faceted approach integrating data-driven
methods and physical modeling to enhance energy efficiency while considering environmental
impacts and climate change. This thesis is structured with four major objectives: (1) identifying the
driving forces behind BEP; (2) predicting and optimizing urban building energy usage with
geographical information and urban heat island (UHI) effects; (3) developing a Gradient-Weighted
LightGBM (GW-LGBM) model to evaluate BEP considering urban morphology; and (4)
conducting an optimization process to identify optimal solutions for improving BEP and thermal
comfort under climate change with a hybrid method that incorporates physical modeling in
Grasshopper and a data-driven approach.
Key findings can be summarized: (1) the geographically and temporally weighted regression
(GTWR) model significantly improves the prediction of building energy performance by capturing
spatio-temporal heterogeneity, identifying key factors such as the number of floors and energy star
ratings, and offering a reliable tool for sustainable urban planning; (2) integrating urban morphology
and building geometry significantly enhances the accuracy of predicting building energy
consumption and GHG emissions, with total gross floor area and natural gas usage identified as the
most influential factors, demonstrating the superiority of the LightGBM model over traditional
methods; (3) integrating a geographically weighted Light Gradient Boosting Machine (GW-LGBM)
model with multi-objective optimization significantly improves the prediction accuracy of building
energy performance and GHG emissions, identifying natural gas usage, total gross floor area,
Energy Star score, and shape form as the most influential factors, achieving up to 89.91%
improvement in energy optimization scenarios. (4) a novel framework combining physical
simulation modeling, explainable machine learning, and multi-objective optimization to predict and
optimize BEP considering urban heat island effects and climate change scenarios. The results
highlight significant improvements in energy use intensity and indoor thermal comfort, with the
hybrid Bayesian optimization-LightGBM model yielding high accuracy and identifying key factors
influencing BEP, thereby offering actionable strategies for sustainable urban development.
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