Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

Valeria Todeschi, Roberto Boghetti, Jérôme H. Kämpf and Guglielmina Mutani
Additional contact information
Valeria Todeschi: Future Urban Legacy Lab—FULL, Department of Energy, Polytechnic of Turin, 10129 Turin, Italy
Roberto Boghetti: Energy Informatics, Idiap Research Institute, 1920 Martigny, Switzerland
Jérôme H. Kämpf: Energy Informatics, Idiap Research Institute, 1920 Martigny, Switzerland
Guglielmina Mutani: Responsible Risk Resilience Centre—R3C, Department of Energy, Polytechnic of Turin, 10129 Turin, Italy

Sustainability, 2021, vol. 13, issue 4, 1-22

Abstract: Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

Keywords: urban energy modeling; energy simulation; urban context; multi-scale; data-driven; machine learning; gradient boosting; engineering model; energy balance; sensitivity analysis; residential buildings; space heating (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/4/1595/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/4/1595/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:4:p:1595-:d:492077

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().