Research Theme: Technology & Innovation

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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Abstract

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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Abstract

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.