Research Product Types: Book Chapter

Abbasabadi, N., & Ashayeri, M. (2024). Machine Learning in Urban Building Energy Modeling. In Abbasabadi, N., & Ashayeri, M. (Eds.), Artificial Intelligence in Performance-Driven Design : Theories, Methods, and Tools: Theories, Methods, and Tools. Wiley-Blackwell.

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Abstract

Urban building energy modeling (UBEM) plays a pivotal role in effective urban energy management and the holistic understanding of citywide energy performance. This book chapter delves into the integration of machine learning (ML) in UBEM, covering applications such as predictive energy consumption modeling and optimization, and providing insights into how ML techniques enhance modeling accuracy and efficiency. It explores current UBEM methods, highlighting their strengths and limitations, and discusses the opportunities presented by ML for advancing UBEM approaches. The chapter also introduces a hybrid UBEM approach that combines data-driven and physics-based simulations to enhance modeling accuracy and reduce uncertainties in capturing urban energy use. This fusion of ML and UBEM offers promising prospects for improving urban energy management practices.

Abbasabadi, N., & Ashayeri, M. (2024). Understanding Social Dynamics in Urban Building and Transportation Energy Behavior. In Abbasabadi, N., & Ashayeri, M. (Eds.), Artificial Intelligence in Performance-Driven Design : Theories, Methods, and Tools: Theories, Methods, and Tools. Wiley-Blackwell.

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This chapter explores the impact of human dynamics, social determinants of public health ( SDPH ), mobility, and occupancy on urban energy use behavior, a topic previously overlooked due to individual buildings or transportation models. A novel, data-driven urban energy model is developed using Artificial Neural Networks ( ANN ), augmented by Garson, Lek's profile and Partial dependence Plot ( PDP ) methods, to holistically evaluate urban energy behavior across Chicago communities, integrating both building and transportation energy use. Utilizing diverse public datasets from the city of Chicago, and validated through cross-validation, the model assesses human dynamics in development of an integrated urban energy modeling. The findings reveal a significant association between SDPH status, mobility, occupancy, and urban energy behavior with household income being a major contributor post accounting for urban spatial patterns and building physical attributes. The study suggests that meeting decarbonization targets in cities requires a broader evaluation encompassing various urban energy determinants. It advocates for emerging technologies and detailed analytical scrutiny, urging researchers and policymakers towards a comprehensive understanding of urban energy use behaviors.

Abbasabadi, N., & Ashayeri, M. (2024). A Hybrid Physics-Based Machine Learning Approach for Integrated Energy and Exposure Modeling. In Abbasabadi, N., & Ashayeri, M. (Eds.), Artificial Intelligence in Performance-Driven Design : Theories, Methods, and Tools: Theories, Methods, and Tools. Wiley-Blackwell.

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Abstract

This chapter introduces a hybrid framework that brings machine learning (ML) and urban big data analytics into integrated modeling of indoor air quality, building operational energy, and ambient airflow dynamics. This holistic approach allows for more effective and accurate simulation results for the design of built environments that prioritize both climate and health considerations. To validate this framework, we undertook a pilot study on a naturally ventilated, large-size office building prototype, as provided by the U.S. Department of Energy. This prototype was hypothetically placed in a densely populated area of Downtown Chicago, IL. For our computations, we employed tools, including EnergyPlus, CONTAM, CFD0, and artificial neural networks (ANNs). The findings highlighted the proposed framework's robust ability to evaluate the effects of building energy efficiency strategies, such as natural ventilation. Additionally, it took into account the indoor concentration of outdoor pollution resulting from the implementation of such strategies. Employing the hybrid approach, we achieved an accuracy characterized by an R -squared value of up to 0.96, facilitated by ANNs. Compared to conventional physics-based simulation methods, the hybrid approach further accelerated the simulation process by up to 200 times. This novel framework offers valuable insights to architects and engineers during early-stage design decisions, enabling them to harmonize occupant health considerations with energy conservation objectives, thereby placing health and well-being at the forefront of decarbonization goals.