Urban models enrichment for energy applications: Challenges in energy simulation using different data sources for building age information. (1st January 2020)

• Record Type: Journal Article
• Authors: Zirak, Maryam; Weiler, Verena; Hein, Martin; Eicker, Ursula
• Is Part Of: Energy. Volume 190(2020)
• Publication Date: 2020-01-01
• Subjects: 3D city model -- Year of construction -- Census data -- Urban heating demand estimation; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116292
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: 3D city models are increasingly used for heating demand analyses at urban scale. Many studies have been done for standardization of required attribute data for energy analysis of buildings. The U-values which can be derived from the building age are one of the main influencing attributes for heat demand modelling. The question remains how building age can be provided. Often, the information on the year of construction of each building is not accessible. On the other hand, statistics about building ages are often available on an aggregated level. This paper compares data provided by municipalities to two statistical data sources: Census 2011 data on municipality level and country-wide statistics for Germany. The result shows building age distribution presented by the census leads to an acceptable total heat demand prediction compared with the results based on the data from the municipality. Therefore, the decision-making at urban level can rely on census data if more detailed information is unavailable or inaccessible. Moreover, the role of refurbishment data is discussed in the paper. Finally, it is recommended to standardise census data for different applications. For energy application, distribution of building age over living area is more demanded than over the number of buildings. Highlights: The year of construction of buildings is a crucial parameter for heating demand calculations. This information is often incomplete or not available. Applying country-wide statistics is not sufficient for individual regions. Regional census data leads to realistic heat demand for two case studies. Refurbished data is important, but missing in statistical data sources.