Details
Original language | English |
---|---|
Article number | 7431939 |
Pages (from-to) | 713-725 |
Number of pages | 13 |
Journal | Proceedings of the IEEE |
Volume | 104 |
Issue number | 4 |
Publication status | Published - Apr 2016 |
Externally published | Yes |
Abstract
Keywords
- Building retrofit, clustering, cost-benefit analysis, district heating, fuzzy logics, geoinformation system (GIS), thermal microgrids (TMGs)
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
Sustainable Development Goals
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In: Proceedings of the IEEE, Vol. 104, No. 4, 7431939, 04.2016, p. 713-725.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Analysis of georeferenced building data for the identification and evaluation of thermal microgrids
AU - Schlueter, Arno
AU - Geyer, Philipp Florian
AU - Cisar, Sasha
N1 - Funding Information: This work was supported in part by the Swiss Commission for Technology and Innovation (CTI).
PY - 2016/4
Y1 - 2016/4
N2 - Retrofitting the existing building stock is among the most important objectives and imperative to meet societal goals to reduce primary energy demand and anthropogenic greenhouse gas emissions. District heating systems have proven to supply heat for buildings both energy and cost efficiently. Thermal microgrids (TMGs) can be understood as a subcategory of district heating systems: small scale, bidirectional, and potentially fed by different thermal sources. Given a suitable combination of loads, the number of and distance between buildings, they can offer economic and environmental advantages compared to the supply by individual heating systems per building. We present a novel method using data analysis techniques on georeferenced building stock data to identify suitable configurations of buildings that yield a cost-efficient TMG. For the identification, both semantic and spatial data from a database are combined using fuzzy logics and cost-benefit analysis. We apply the method using a case study featuring a database of 306 buildings potentially to be retrofitted. As a result, we can identify nine groups of 25 buildings that would form a microgrid featuring up to 17.4% cost benefits compared to an individual heat supply. This would save approximately 30% of the building-induced CO2 emission of the community.
AB - Retrofitting the existing building stock is among the most important objectives and imperative to meet societal goals to reduce primary energy demand and anthropogenic greenhouse gas emissions. District heating systems have proven to supply heat for buildings both energy and cost efficiently. Thermal microgrids (TMGs) can be understood as a subcategory of district heating systems: small scale, bidirectional, and potentially fed by different thermal sources. Given a suitable combination of loads, the number of and distance between buildings, they can offer economic and environmental advantages compared to the supply by individual heating systems per building. We present a novel method using data analysis techniques on georeferenced building stock data to identify suitable configurations of buildings that yield a cost-efficient TMG. For the identification, both semantic and spatial data from a database are combined using fuzzy logics and cost-benefit analysis. We apply the method using a case study featuring a database of 306 buildings potentially to be retrofitted. As a result, we can identify nine groups of 25 buildings that would form a microgrid featuring up to 17.4% cost benefits compared to an individual heat supply. This would save approximately 30% of the building-induced CO2 emission of the community.
KW - Building retrofit
KW - clustering
KW - cost-benefit analysis
KW - district heating
KW - fuzzy logics
KW - geoinformation system (GIS)
KW - thermal microgrids (TMGs)
UR - http://www.scopus.com/inward/record.url?scp=84960510102&partnerID=8YFLogxK
U2 - 10.1109/JPROC.2016.2526118
DO - 10.1109/JPROC.2016.2526118
M3 - Article
AN - SCOPUS:84960510102
VL - 104
SP - 713
EP - 725
JO - Proceedings of the IEEE
JF - Proceedings of the IEEE
SN - 0018-9219
IS - 4
M1 - 7431939
ER -