Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development
Urban building energy models (UBEMs), developed to understand the energy performance of building stocks of a region, can aid in key decisions related to energy policy and climate change solutions. However, creating a city-scale UBEM is challenging due to the requirements of diverse geometric and non...
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| Aineistotyyppi: | Artikkeli |
| Kieli: | English |
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MDPI AG
2024-04-01
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| Sarja: | Buildings |
| Aiheet: | |
| Linkit: | https://www.mdpi.com/2075-5309/14/5/1241 |
| _version_ | 1827244716908871680 |
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| author | Md. Uzzal Hossain Isabella Cicco Melissa M. Bilec |
| author_facet | Md. Uzzal Hossain Isabella Cicco Melissa M. Bilec |
| author_sort | Md. Uzzal Hossain |
| collection | DOAJ |
| description | Urban building energy models (UBEMs), developed to understand the energy performance of building stocks of a region, can aid in key decisions related to energy policy and climate change solutions. However, creating a city-scale UBEM is challenging due to the requirements of diverse geometric and non-geometric datasets. Thus, we aimed to further elucidate the process of creating a UBEM with disparate and scarce data based on a bottom-up, physics-based approach. We focused on three typically overlooked but functionally important commercial building stocks, which are sales and shopping, healthcare facilities, and food sales and services, in the region of Pittsburgh, Pennsylvania. We harvested relevant local building information and employed photogrammetry and image processing. We created archetypes for key building types, designed 3D buildings with SketchUp, and performed an energy analysis using EnergyPlus. The average annual simulated energy use intensities (EUIs) were 528 kWh/m<sup>2</sup>, 822 kWh/m<sup>2</sup>, and 2894 kWh/m<sup>2</sup> for sales and shopping, healthcare facilities, and food sales and services, respectively. In addition to variations found in the simulated energy use pattern among the stocks, considerable variations were observed within buildings of the same stock. About 9% and 11% errors were observed for sales and shopping and healthcare facilities when validating the simulated results with the actual data. The suggested energy conservation measures could reduce the annual EUI by 10–26% depending on the building use type. The UBEM results can assist in finding energy-efficient retrofit solutions with respect to the energy and carbon reduction goal for commercial building stocks at the city scale. The limitations highlighted may be considered for higher accuracy, and the UBEM has a high potential to integrate with urban climate and energy models, circular economy, and life cycle assessment for sustainable urban planning. |
| first_indexed | 2025-03-21T22:32:36Z |
| format | Article |
| id | doaj.art-df9f4cbbba724a9586c2093b66e98d3a |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2025-03-21T22:32:36Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-df9f4cbbba724a9586c2093b66e98d3a2024-05-24T13:12:44ZengMDPI AGBuildings2075-53092024-04-01145124110.3390/buildings14051241Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype DevelopmentMd. Uzzal Hossain0Isabella Cicco1Melissa M. Bilec2Department of Civil and Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara St., Pittsburgh, PA 15261, USADepartment of Civil and Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara St., Pittsburgh, PA 15261, USADepartment of Civil and Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara St., Pittsburgh, PA 15261, USAUrban building energy models (UBEMs), developed to understand the energy performance of building stocks of a region, can aid in key decisions related to energy policy and climate change solutions. However, creating a city-scale UBEM is challenging due to the requirements of diverse geometric and non-geometric datasets. Thus, we aimed to further elucidate the process of creating a UBEM with disparate and scarce data based on a bottom-up, physics-based approach. We focused on three typically overlooked but functionally important commercial building stocks, which are sales and shopping, healthcare facilities, and food sales and services, in the region of Pittsburgh, Pennsylvania. We harvested relevant local building information and employed photogrammetry and image processing. We created archetypes for key building types, designed 3D buildings with SketchUp, and performed an energy analysis using EnergyPlus. The average annual simulated energy use intensities (EUIs) were 528 kWh/m<sup>2</sup>, 822 kWh/m<sup>2</sup>, and 2894 kWh/m<sup>2</sup> for sales and shopping, healthcare facilities, and food sales and services, respectively. In addition to variations found in the simulated energy use pattern among the stocks, considerable variations were observed within buildings of the same stock. About 9% and 11% errors were observed for sales and shopping and healthcare facilities when validating the simulated results with the actual data. The suggested energy conservation measures could reduce the annual EUI by 10–26% depending on the building use type. The UBEM results can assist in finding energy-efficient retrofit solutions with respect to the energy and carbon reduction goal for commercial building stocks at the city scale. The limitations highlighted may be considered for higher accuracy, and the UBEM has a high potential to integrate with urban climate and energy models, circular economy, and life cycle assessment for sustainable urban planning.https://www.mdpi.com/2075-5309/14/5/1241urban building energy modelingmethodological frameworkarchetype developmentimage processingLiDAR analysis |
| spellingShingle | Md. Uzzal Hossain Isabella Cicco Melissa M. Bilec Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development Buildings urban building energy modeling methodological framework archetype development image processing LiDAR analysis |
| title | Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development |
| title_full | Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development |
| title_fullStr | Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development |
| title_full_unstemmed | Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development |
| title_short | Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development |
| title_sort | advancing urban building energy modeling building energy simulations for three commercial building stocks through archetype development |
| topic | urban building energy modeling methodological framework archetype development image processing LiDAR analysis |
| url | https://www.mdpi.com/2075-5309/14/5/1241 |
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