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Excell, Lauren E.,

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Machine Learning and Data-Driven Evaluation of Retrofit Pathways for Equitable Urban Building Energy Performance /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning and Data-Driven Evaluation of Retrofit Pathways for Equitable Urban Building Energy Performance // Lauren E Excell.
Author:	Excell, Lauren E.,
Description:	1 electronic resource (164 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
Subject:	Built environment. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31757690
ISBN:	9798346785002

Machine Learning and Data-Driven Evaluation of Retrofit Pathways for Equitable Urban Building Energy Performance /
Excell, Lauren E.,

Machine Learning and Data-Driven Evaluation of Retrofit Pathways for Equitable Urban Building Energy Performance /Lauren E Excell. - 1 electronic resource (164 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

In recent years, the conversations around climate action plans and environmental justice have become increasingly intertwined, showing a growing understanding of how our built environment impacts the planet and the people around us. Cities have begun addressing equity more seriously in their climate action plans, often creating specific plans and initiatives to address historical inequities in urban development. These social inequities are particularly apparent in buildings and urban morphology; in disadvantaged communities, buildings are less efficient and there are fewer public green spaces. Any conversations about developing or redeveloping the urban environment must now consider how the people and the planet are impacted. The overarching motivation for this dissertation is to lay the foundations for frameworks and methods for evaluating equitable decarbonization pathways at building- and urban-scales.A key focus area of this dissertation is taking advantage of open data sources to make the methods presented here readily replicable by a global audience. Open source data includes satellite imagery made available through initiatives at Google Earth, Dynamic World, and OpenStreetMaps, information on buildings and the built environment provided by local governments, and socioeconomic and demographic information from the US Census. Beyond this, the resources used to analyze these data and perform energy modeling are all open source and free to use (i.e., EnergyPlus, Python, R). By utilizing data and resources that are free to the public, this research aims to improve the accessibility of retrofit analyses so that they can be used in disadvantaged and advantaged communities alike.The first area I investigate is the existence of an energy equity gap in institutional buildings in the United States, and whether building system retrofits and vegetation can improve energy efficiency and reduce the energy equity gap. In this work, I use a retrofit dataset made publicly available by the California Energy Commission as a part of the Proposition 39 initiative, which provided funding for energy efficiency measures to primary and secondary school buildings in California. The effect of vegetation was evaluated using the Normalized Difference Vegetation Index (NDVI), obtained from Landsat8 data from Google Earth Engine. Regression analyses and statistical significance tests were used to evaluate the effectiveness of retrofits and vegetation in improving energy performance over time.Once it was shown that both retrofits and vegetation are able to increase energy efficiency and improve energy equity, I apply this knowledge to create an urban building energy modeling (UBEM) framework for evaluating building and district-scale retrofits across disparate neighborhoods. The aim of this study was to demonstrate the varied effects that vegetation and building-scale retrofits can have in advantaged vs disadvantaged communities because of the inequities in their built environments. I utilize publicly available information from New York City and Los Angeles to demonstrate this UBEM framework by evaluating building-scale retrofits (i.e. full window retrofit) and district-scale retrofits and policy (i.e. increasing urban green space and reducing car traffic). To examine the effect of the varied urban morphologies of disparate neighborhoods and climate zones, I utilize satellite data from Dynamic World to adapt weather files via microclimate simulation.

English

ISBN: 9798346785002Subjects--Topical Terms:

3634048
Built environment.

Machine Learning and Data-Driven Evaluation of Retrofit Pathways for Equitable Urban Building Energy Performance /
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520 $a In recent years, the conversations around climate action plans and environmental justice have become increasingly intertwined, showing a growing understanding of how our built environment impacts the planet and the people around us. Cities have begun addressing equity more seriously in their climate action plans, often creating specific plans and initiatives to address historical inequities in urban development. These social inequities are particularly apparent in buildings and urban morphology; in disadvantaged communities, buildings are less efficient and there are fewer public green spaces. Any conversations about developing or redeveloping the urban environment must now consider how the people and the planet are impacted. The overarching motivation for this dissertation is to lay the foundations for frameworks and methods for evaluating equitable decarbonization pathways at building- and urban-scales.A key focus area of this dissertation is taking advantage of open data sources to make the methods presented here readily replicable by a global audience. Open source data includes satellite imagery made available through initiatives at Google Earth, Dynamic World, and OpenStreetMaps, information on buildings and the built environment provided by local governments, and socioeconomic and demographic information from the US Census. Beyond this, the resources used to analyze these data and perform energy modeling are all open source and free to use (i.e., EnergyPlus, Python, R). By utilizing data and resources that are free to the public, this research aims to improve the accessibility of retrofit analyses so that they can be used in disadvantaged and advantaged communities alike.The first area I investigate is the existence of an energy equity gap in institutional buildings in the United States, and whether building system retrofits and vegetation can improve energy efficiency and reduce the energy equity gap. In this work, I use a retrofit dataset made publicly available by the California Energy Commission as a part of the Proposition 39 initiative, which provided funding for energy efficiency measures to primary and secondary school buildings in California. The effect of vegetation was evaluated using the Normalized Difference Vegetation Index (NDVI), obtained from Landsat8 data from Google Earth Engine. Regression analyses and statistical significance tests were used to evaluate the effectiveness of retrofits and vegetation in improving energy performance over time.Once it was shown that both retrofits and vegetation are able to increase energy efficiency and improve energy equity, I apply this knowledge to create an urban building energy modeling (UBEM) framework for evaluating building and district-scale retrofits across disparate neighborhoods. The aim of this study was to demonstrate the varied effects that vegetation and building-scale retrofits can have in advantaged vs disadvantaged communities because of the inequities in their built environments. I utilize publicly available information from New York City and Los Angeles to demonstrate this UBEM framework by evaluating building-scale retrofits (i.e. full window retrofit) and district-scale retrofits and policy (i.e. increasing urban green space and reducing car traffic). To examine the effect of the varied urban morphologies of disparate neighborhoods and climate zones, I utilize satellite data from Dynamic World to adapt weather files via microclimate simulation.
546 $a English
590 $a School code: 0212
650 4 $a Built environment. $3 3634048
650 4 $a Sensitivity analysis. $3 3560752
650 4 $a Greenhouse gases. $3 797971
650 4 $a Emissions. $3 3559499
650 4 $a Buildings. $3 861672
650 4 $a Electric rates. $3 3560701
650 4 $a Neighborhoods. $3 993475
650 4 $a Energy modeling. $3 3683859
650 4 $a Climate change. $2 bicssc $3 2079509
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710 2 $a Stanford University. $e degree granting institution. $3 3765820
720 1 $a Jain, Rishee $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-06B.
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791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31757690