Developing a Data-Driven Framework for MultiScale Integrated Urban Building and Transportation Energy Modeling

Authors

  • Narjes Abbasabadi University of Texas at Arlington

Abstract

This article proposes an integrated data-driven framework for urban energy use modeling (UEUM) that enables providing a holistic image of urban energy use at multiple scales. The UEUM allows aggregating across end-uses, building, and transportation. With considering urban socio-spatial context, it gives insight into the multifaceted and intricate relationships between urban key attributes, and building and transportation energy performance. This model helps predict urban energy performance more precisely by reducing the simulation uncertainties through using disaggregated and spatially explicit data and applying artificial intelligence (AI) techniques. In addition to increasing the accuracy, the model facilitates reducing the execution time for an urban scale energy modeling. The framework was evaluated using Chicago, Illinois, a major city in the US, as a case study. The results for Chicago demonstrate the feasibility of this approach. Among the tested AI algorithms, k-nearest neighbor performed as the best model in terms of accuracy for a single-output model while artificial neural network algorithm showed the best overall performance for the integrated building and transportation energy use modeling.

Author Biography

  • Narjes Abbasabadi, University of Texas at Arlington

    Narjes Abbasabadi, Ph.D., is an architect, researcher, and educator. She earned her Ph.D. in Architecture from the Illinois Institute of Technology (IIT). She also holds Master and Bachelor degrees in architecture from Tehran Azad University. Prior to joining UTA, she taught in the College of Architecture at IIT. Dr. Abbasabadi’s research investigates sustainability, ambient intelligence, and computation in the built environment. Much of her work has focused on developing data-driven and simulation-based frameworks and tools for multi-scale analysis and modeling of urban and building systems and environmental impact assessment. Particularly, she explores integrated human and energy feedback systems to enable dynamic exploration of performance-driven and user-centric design for achieving energy-efficient, resilient, equitable, and healthy cities.

    Dr. Abbasabadi received prestigious honors and awards, including “ARCC Dissertation Award Honorable Mention” (Architectural Research Centers Consortium (ARCC), 2020), “Best Ph.D. Program Dissertation Award” (IIT CoA, 2019), and 2nd place in the U.S. Department of Energy (DOE)’s Race to Zero Design Competition (DOE, 2018). In 2018, she organized the 3rd IIT CoA International Symposium on Buildings, Cities, and Performance. She serves as editor of the third issue of Prometheus Journal, which received the 2020 Haskell Award from AIA New York, Center for Architecture. She has also received grants for the development of design codes and prototypes for low-carbon buildings. Her work has been published in premier journals, including Applied Energy, Building and Environment, and Energy and Buildings. She presented her work at academic conferences, including ARCC 2019 (shortlisted for Best Paper Award), SimAuD 2019, and the 2019 Rosenfeld Symposium, Lawrence Berkeley National Lab. She has also contributed chapters to books such as Architectural Research Addressing Societal Challenges. Dr. Abbasabadi has also practiced with several firms and institutions and led several design and research projects. Most recently, she practiced as an architect with Adrian Smith + Gordon Gill Architecture (AS+GG), where she has been involved in major sustainable projects such as the 2020 World Expo.

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Published

2019-11-16

Issue

Vol. 3 (2019): Buildings, Cities, and Performance I

Section

Articles