A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making
| Autor: | Mohammad Haris Shamsi, Mark Bohacek, Eleni Mangina, Cathal Hoare, Karl Purcell, James O'Donnell, Usman Ali |
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| Rok vydání: | 2020 |
| Předmět: |
Geographic information system
Operations research Computer science 020209 energy 02 engineering and technology Management Monitoring Policy and Law computer.software_genre Environmental data 020401 chemical engineering Urban planning Machine learning 0202 electrical engineering electronic engineering information engineering 0204 chemical engineering Energy performance certificate business.industry Data-driven approaches Mechanical Engineering Building energy performance Building and Construction Energy consumption Energy planning General Energy Geocoding GIS modeling business computer Urban building energy modeling Data integration Efficient energy use |
| Zdroj: | Applied Energy. 279:115834 |
| ISSN: | 0306-2619 |
| DOI: | 10.1016/j.apenergy.2020.115834 |
| Popis: | 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. Science Foundation Ireland University College Dublin ESIPP UCD |
| Databáze: | OpenAIRE |
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