A community-based decision support map for building retrofit towards a more sustainable future
The energy sector is the source of nearly three-quarters of the global greenhouse gas emissions and buildings form around one-third of it. The largest share of the energy consumption in the buildings belongs to the heating systems which are mostly dependent on fossil fuels. To keep up with the demands of increasing population, it is vital to undergo clean energy transformations effective immediately. In this study, a decision support map was created for a selected community to ease the energy retrofit candidate selection process. Buildings are assessed considering sustainability level of the heating infrastructure, energy use intensity and age, and ranked according to their goodness level on the energy efficiency scale through analytic hierarchy process. Finally, a multi-layered map is created to visualize the outcomes. The decision support map fills a gap in the existing tool landscape as it offers energy consulting on a communal (i.e. not only single building or quarter) level and does not require work from the user as it relies on public data. The results showed that new buildings constructed by environmentally responsible codes tend to have heating systems mostly powered by renewable sources and less level of energy threshold violations while old buildings generally act in the other direction. As the main outcome, the created visuals support the user to have insights about the accumulation areas of the households in low rankings as well as the energy efficiency standing of the individual buildings in comparison with the neighbouring households.
Authors
- E. Linder (ORCID: https://orcid.org/0000-0002-4527-1010)
- Ezgi Köker Gökgöl
- Philipp Schuetz (ORCID: https://orcid.org/0000-0001-7564-1468)
Institutions
- Lucerne University of Applied Sciences and Arts (CH)
Publication Details
- Journal
- Energy and Buildings
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1016/j.enbuild.2025.115554
- Citations
- 4
- Primary Topic
- Building Energy and Comfort Optimization
- Type
- article
- Field-Weighted Citation Impact
- 4.03