A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making

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Title: A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making
Authors: Ali, UsmanShamsi, Mohammad HarisBohacek, MarkPurcell, KarlHoare, CathalMangina, EleniO'Donnell, James
Permanent link: http://hdl.handle.net/10197/12265
Date: 1-Dec-2020
Online since: 2021-06-21T11:45:02Z
Abstract: Urban planners, local authorities, and energy policymakers often develop strategic sustainable energy plans for the urban building stock in order to minimize overall energy consumption and emissions. Planning at such scales could be informed by building stock modeling using existing building data and Geographic Information System-based mapping. However, implementing these processes involves several issues, namely, data availability, data inconsistency, data scalability, data integration, geocoding, and data privacy. This research addresses the aforementioned information challenges by proposing a generalized integrated methodology that implements bottom-up, data-driven, and spatial modeling approaches for multi-scale Geographic Information System mapping of building energy modeling. This study uses the Irish building stock to map building energy performance at multiple scales. The generalized data-driven methodology uses approximately 650,000 Irish Energy Performance Certificates buildings data to predict more than 2 million buildings’ energy performance. In this case, the approach delivers a prediction accuracy of 88% using deep learning algorithms. These prediction results are then used for spatial modeling at multiple scales from the individual building level to a national level. Furthermore, these maps are coupled with available spatial resources (social, economic, or environmental data) for energy planning, analysis, and support decision-making. The modeling results identify clusters of buildings that have a significant potential for energy savings within any specific region. Geographic Information System-based modeling aids stakeholders in identifying priority areas for implementing energy efficiency measures. Furthermore, the stakeholders could target local communities for retrofit campaigns, which would enhance the implementation of sustainable energy policy decisions.
Funding Details: Science Foundation Ireland
University College Dublin
Funding Details: ESIPP UCD
Type of material: Journal Article
Publisher: Elsevier
Journal: Applied Energy
Volume: 279
Copyright (published version): 2020 Elsevier
Keywords: GIS modelingMachine learningUrban planningData-driven approachesBuilding energy performanceUrban building energy modelingEnergy performance certificate
DOI: 10.1016/j.apenergy.2020.115834
Language: en
Status of Item: Peer reviewed
ISSN: 0306-2619
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by/3.0/ie/
Appears in Collections:Mechanical & Materials Engineering Research Collection
Computer Science Research Collection
Energy Institute Research Collection

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