Next Article in Journal / Special Issue
Detailed Streetspace Modelling for Multiple Applications: Discussions on the Proposed CityGML 3.0 Transportation Model
Previous Article in Journal
Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets
Previous Article in Special Issue
The City of Tomorrow from… the Data of Today
Open AccessArticle

Concept and Evaluation of Heating Demand Prediction Based on 3D City Models and the CityGML Energy ADE—Case Study Helsinki

1
Department of Informatics, Stuttgart University of Applied Sciences, 70174 Stuttgart, Germany
2
City of Helsinki, FI-00130 Helsinki, Finland
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(10), 602; https://doi.org/10.3390/ijgi9100602
Received: 31 August 2020 / Revised: 29 September 2020 / Accepted: 4 October 2020 / Published: 12 October 2020
(This article belongs to the Special Issue The Applications of 3D-City Models in Urban Studies)
This work presents a concept for heating demand and resulting CO2 emissions prediction based on a 3D city model in CityGML format in various scenarios under the consideration of a changing climate. In the case study of Helsinki, the Helsinki Energy and Climate Atlas, that provides detailed information for individual buildings conducting the heating demand, is integrated into the 3D city model using the CityGML Energy Application Domain Extension (Energy ADE) to provide energy-relevant information based on a standardized data model stored in a CityGML database, called 3DCityDB. The simulation environment SimStadt is extended to retrieve the information stored within the Energy ADE schema, use it during simulations, and write simulation results back to the 3DCityDB. Due to climate change, a heating demand reduction of 4% per decade is predicted. By 2035, a reduction of 0.7 TWh is calculated in the normal and of 1.5 TWh in the advanced refurbishment scenario. Including the proposed improvements of the district heating network, heating CO2 emissions are predicted to be reduced by up to 82% by 2035 compared to 1990. The City of Helsinki’s assumed heating demand reduction through the modernization of 2.0 TWh/a by 2035 is not achieved with a 3% refurbishment rate. Furthermore, the reduction of CO2 emissions is mainly achieved through lower CO2 emission factors of the district heating network in Helsinki. View Full-Text
Keywords: 3D city model; CityGML; 3DCityDB; EnergyADE; SimStadt; CO2 emission; energy demand prediction 3D city model; CityGML; 3DCityDB; EnergyADE; SimStadt; CO2 emission; energy demand prediction
Show Figures

Graphical abstract

MDPI and ACS Style

Rossknecht, M.; Airaksinen, E. Concept and Evaluation of Heating Demand Prediction Based on 3D City Models and the CityGML Energy ADE—Case Study Helsinki. ISPRS Int. J. Geo-Inf. 2020, 9, 602.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop