Title: A Hybrid Physics‐Based Machine Learning Approach for Integrated Energy and Exposure Modeling
Abstract: Chapter 3 A Hybrid Physics-Based Machine Learning Approach for Integrated Energy and Exposure Modeling Mehdi Ashayeri, Mehdi Ashayeri School of Architecture, College of Arts and Media, Southern Illinois University Carbondale, Carbondale, IL, USASearch for more papers by this authorNarjes Abbasabadi, Narjes Abbasabadi Department of Architecture, School of Architecture, College of Built Environments, University of Washington, Seattle, WA, USASearch for more papers by this author Mehdi Ashayeri, Mehdi Ashayeri School of Architecture, College of Arts and Media, Southern Illinois University Carbondale, Carbondale, IL, USASearch for more papers by this authorNarjes Abbasabadi, Narjes Abbasabadi Department of Architecture, School of Architecture, College of Built Environments, University of Washington, Seattle, WA, USASearch for more papers by this author Book Editor(s):Narjes Abbasabadi, Narjes Abbasabadi University of Washington, Seattle, USASearch for more papers by this authorMehdi Ashayeri, Mehdi Ashayeri Southern Illinois University Carbondale, Carbondale, USASearch for more papers by this author First published: 19 April 2024 https://doi.org/10.1002/9781394172092.ch3 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary 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. References Abbasabadi , N. and Ashayeri , M. ( 2019 ). Urban Energy Use Modeling Methods and Tools: A Review and an Outlook . Building and Environment 161 ( August ): 106270 . https://doi.org/10.1016/j.buildenv.2019.106270 . 10.1016/j.buildenv.2019.106270 Google Scholar Abbasabadi , N. , Ashayeri , M. , Azari , R. et al. ( 2019 ). An Integrated Data-Driven Framework for Urban Energy Use Modeling (UEUM) . 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Publication Year: 2024
Publication Date: 2024-04-19
Language: en
Type: other
Indexed In: ['crossref']
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