Liu, Y., & Abbasabadi, N. (2025). Enhancing urban building energy models with Vision Transformers: A Case study in material classification from Google street view. Energy and Buildings, 333, Article 115457. https://doi.org/10.1016/j.enbuild.2025.115457.
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Abstract
The growing urbanization and increased urban energy consumption highlight the need for energy use and greenhouse gas emissions reduction strategies. Urban Building Energy Modeling (UBEM) emerged as a valuable tool for managing and optimizing energy consumption at the neighborhood and city scales to support carbon reduction goals. However, the accuracy of the UBEM is often limited by the lack of large-scale building façade material dataset. This study introduces a new approach to enhance UBEM by integrating an automatic deep learning material classification pipeline. The pipeline leverages multiple views of Google Street View Images (SVIs) to extract building façade material information, utilizing two Swin Vision Transformer (ViT) models to capture both global and local features from the SVIs. The pipeline achieved a main material classification accuracy reached 97.08%, and the sub-category accuracy reached 91.56% in a multi-class classification task. As the first study to apply a deep learning model for material classification to enhance the UBEM framework, this work was tested on the University of Washington campus, which features diverse facade materials. The model demonstrated its effectiveness by achieving an overall accuracy increase of 11.4% in year-round total operational energy simulations. The scalability of this material classification pipeline enables a more accurate and cost-effective application of UBEM at broader urban scales.
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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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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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.
Ashayeri, M., Piri, S., & Abbasabadi, N. (2024). Exploring U.S. Occupant Perception Toward Indoor Air Quality Via Social Media and NLP Analysis. Journal of Environmental Science and Public Health, 8(2). https://doi.org/10.26502/jesph.96120205.
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The global implementation of stay-at-home mandates altered people's activities within the built environment, prompting a slowdown in the spread of covid viruses. Nevertheless, this period shed light on previously unforeseen challenges in achieving "better" indoor air quality (IAQ) within buildings, necessitating a focus on building health resilience for future scenarios. This study aims to evaluate occupants' feedback on the impact of stay-at-home measures on IAQ perception in buildings across the U.S. during the first year of the pandemic (2020) and compare it with the baseline from the previous year (2019) nationwide to assess the changes and identify potential areas for IAQ management strategies. Geo-tagged textual data from X (formerly known as Twitter) platform were collected and analyzed using Natural Language Processing (NLP) based on time series sentiment analysis techniques to compute the feedback. Findings indicate that occupants’ negative feedback on IAQ increased during 2020 compared to the baseline. It was also found that public perception of IAQ in 2020 was notably less favorable, potentially due to deteriorating conditions inside homes as people spent more time indoors. The study underscores the potential of NLP in capturing occupant perception, contributing to data-driven studies that can inform design, engineering, and policy-making for sustainable future.
Keywords
Indoor Air Quality; Occupant Perception; COVID Stay-athome; Natural Language Processing (NLP); Time Series Sentiment Analysis
Worthy, A., Ashayeri, M., Marshall, J., & Abbasabadi, N. (2025). Bridging the simulation-to-reality gap: A comprehensive review of microclimate integration in urban building energy modeling (UBEM). Energy and Buildings, 331, Article 115392. https://doi.org/10.1016/j.enbuild.2025.115392.
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Buildings are significant contributors to global energy consumption, necessitating urgent action to reduce energy use and associated emissions. Urban Building Energy Modeling (UBEM) is a critical tool that provides essential insights into citywide building energy dynamics though generating quantitative energy data and enabling holistic analysis and optimization of energy systems. However, current UBEM methodologies and tools are constrained by their reliance on non-urban-specific and aggregated climate data inputs, leading to discrepancies between modeled and actual energy expenditures. This article presents a comprehensive review of the datasets, tools, methodologies, and novel case studies deployed to integrate microclimates into UBEMs, aiming to bridge the modeling gap and to address the uncertainties due to the absence of real-world microclimate data in the models. It expands beyond conventional methods by elaborating on substitutional observational-based and simulation-based datasets, addressing their spatial and temporal tradeoffs. The review highlights that while remote sensing technologies are extensively utilized for building geometric data UBEM inputs, there remains an underexplored potential in reanalysis and observational-based products for environmental data; specifically, for the inclusion of parameters that are conventionally not included in UBEM analysis such as tree canopy coverage and land surface temperature. Furthermore, adopting a hybrid methodology, which combines observational and simulation-based datasets, may be a promising approach for more accurately representing microclimate conditions in UBEMs; as this process would ensure more representative climate parameter inputs and ground-truthing, while effectively managing computational demands across extensive temporal and spatial simulations. This could be achieved through integrating local earth observation datasets with computational fluid dynamics (CFD) tools or by merging local earth observational data with simulation-based reanalysis products and coupling these weather inputs with simulation-based building energy management models. Finally, this review underscores the importance of validating UBEMs with local microclimate weather data to ensure that model results are actionable, reliable, and accurate.
Worthy, A., Ashayeri, M., & Abbasabadi, N. (2025). Leveraging earth observational data products and machine learning to enhance urban building energy modeling (UBEM) with microclimate effects. Sustainable Cities and Society, 130, Article 106544. https://doi.org/10.1016/j.scs.2025.106544.
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Urban Building Energy Modeling (UBEM) is a powerful tool used for sustainable design, urban planning, and efficient energy management, as it provides essential insights into the building energy consumption patterns. However, current UBEM methodologies often lack urban-specific microclimate data, leading to discrepancies between modeled and actual energy consumption. This research develops a bottom-up statistical UBEM framework that combines and integrates earth observational climate data, climate reanalysis products, and annual energy usage data, measured by the Seattle Energy Benchmarking Dataset, to capture the impacts of microclimates on urban building energy performance. Using machine learning techniques and Seattle, Washington, USA as a proof of concept, our results demonstrate that incorporating urban-specific microclimate data substantially enhances building energy modeling prediction accuracy. Specifically, three model variable schemas are compared; the optimal model incorporating earth observational data achieved a 0.16 (from 0.55 to 0.71) increase in testing R2 over the model that did not include any climate data inputs, and a 0.056 (from 0.66 to 0.71) increase in testing R2, over the model that included TMY3 climate data inputs. These findings validate the use of earth observational datasets for urban building energy modeling to include microclimate effects. Furthermore, machine learning algorithms outperform traditional linear methods, with respective ordered rankings: CATBoost, XGBoost, Random Forest, Decision Trees, and Linear Regression. Our study underscores the importance of integrating microclimate data into UBEM frameworks and advocates for the expanded use of earth observational and remote sensing datasets for mitigation of simulation-to-reality discrepancies; to ultimately inform more accurate energy-driven design and planning strategies at the city level.
In FY24, CBE researchers have been awarded a number of grants and contracts for projects that include a community engagement component, defined as “collaboration between institutions of higher education and their larger communities (local, regional/state, national, global) for the mutually beneficial creation and exchange of knowledge and resources in a context of partnership and reciprocity,” by The Carnegie Foundation for the Advancement of Teaching. In FY24 (July 2023 – June 2024), CBE researchers were awarded 17 grant and contract awards,…
Abbasabadi, N., & Ashayeri, M. (2024). From Tweets to Energy Trends (TwEn): An exploratory framework for machine learning-based forecasting of urban-scale energy behavior leveraging social media data. Energy and Buildings, 317, 114440-. https://doi.org/10.1016/j.enbuild.2024.114440
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Abstract
TwEn framework links tweet frequency with urban energy use patterns. AI models forecast energy from social media data sourced from X. Framework predicts NYC electricity use with high accuracy. Tweet frequency’s seasonal stability enhances energy research. Real-time social data can aid sustainable urban energy policy. Understanding energy behavior is crucial in addressing climate change, yet the accuracy of energy predictions is often limited by reliance on oversimplified occupancy data. This study develops an exploratory framework, from Tweets to Energy Trends (TwEn), leveraging machine learning and geo-tagged social media data to investigate the social dynamics of urban energy behavior. TwEn explores the relationship between social media interactions, specifically the frequency of tweets using data from the X Platform, and energy use patterns on an hourly basis. Employing various machine learning models, including artificial neural networks (ANN), decision tree (DTREE), random forest (RDF), and gradient boosting machine (GBM), the study evaluates their efficiency in both static and time-series forecasting of energy use trends and investigates the capability of social media data in predicting urban energy patterns. In addition, the study carries out a series of sensitivity analyses to provide an examination of the data and models. Furthermore, comprehensive data acquisition methods are developed and implemented. Tested on New York City using actual hourly electricity consumption data, the framework demonstrates significant predictive power of tweet frequency on urban electricity use. The framework also exhibits significant seasonality in X data, identifying patterns and trends that can inform time series urban building energy models (UBEM). The results offer new insights into the determinants of urban energy behavior and provide crucial perspectives for augmenting UBEMs, ensuring they are closely aligned with the complex social dynamics of contemporary urban environments. By integrating both digital and physical data, this study sheds light on urban energy behavior, supporting the formulation of effective and sustainable energy policies for urban futures.
Keywords
Occupancy; Social Media; Time Series Forecast; Urban Building Energy Modeling (UBEM); Urban Energy Behavior
The Inaugural CBE Climate Solutions Symposium took place on May 23, 2024. The event began with a reception and poster session, followed by an invited lecture “Every Project is a Climate Opportunity” with Don Davies, PE, SE and Joan Crooks. 36 research posters were submitted and accepted to the symposium. The posters covered a range of topics, from affordable housing in Indonesia (Bella Septianti, Architecture/Design Technology), to CLT and structural steel comparative lifecycle assessment (Mira Malden, Community, Environment, and Planning)….