CITY-WIDE TOTAL ENERGY DEMAND ESTIMATION OF BUILDINGS USING SEMANTIC 3D CITY MODELS AND STATISTICAL DATA

21 citations as recorded by crossref.

1. Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems B. Schwab et al. 10.3390/su12093799
2. Joint optimization of building-envelope and heating-system retrofits at territory scale to enhance decision-aiding A. Rogeau et al. 10.1016/j.apenergy.2020.114639
3. Applications of 3D City Models: State of the Art Review F. Biljecki et al. 10.3390/ijgi4042842
4. Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling M. Wurm et al. 10.3390/ijgi10010023
5. Implementation of CityGML energy application domain extension (ADE) for integration of urban solar potential indicators using object-oriented modelling approach P. Wate & S. Saran 10.1080/10106049.2015.1034192
6. Adaptation of building envelope models for energy simulation at district scale L. Frayssinet et al. 10.1016/j.egypro.2017.07.327
7. Parametric Assessment of Building Heating Demand for Different Levels of Details and User Comfort Levels: A Case Study in London, UK A. Apostolopoulou et al. 10.3390/su15108374
8. Exploiting the Potential of Integrated Public Building Data: Energy Performance Assessment of the Building Stock in a Case Study in Northern Italy A. Pasquinelli et al. 10.3390/ijgi8010027
9. CityGML in the Integration of BIM and the GIS: Challenges and Opportunities Y. Tan et al. 10.3390/buildings13071758
10. Large-scale estimation of buildings’ thermal load using LiDAR data M. Bizjak et al. 10.1016/j.enbuild.2020.110626
11. 3D Viewpoint Management and Navigation in Urban Planning: Application to the Exploratory Phase R. Neuville et al. 10.3390/rs11030236
12. Characterizing and structuring urban GIS data for housing stock energy modelling and retrofitting S. HosseiniHaghighi et al. 10.1016/j.enbuild.2021.111706
13. Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow A. Nutkiewicz et al. 10.1016/j.apenergy.2018.05.023
14. The potential of remote sensing and GIS in urban building energy modelling A. Anand & C. Deb 10.1016/j.enbenv.2023.07.008
15. TEASER: an open tool for urban energy modelling of building stocks P. Remmen et al. 10.1080/19401493.2017.1283539
16. The variants of an LOD of a 3D building model and their influence on spatial analyses F. Biljecki et al. 10.1016/j.isprsjprs.2016.03.003
17. 3D Data Models for Urban Energy Simulation P. Wate & V. Coors 10.1016/j.egypro.2015.11.753
18. Urban energy use modeling methods and tools: A review and an outlook N. Abbasabadi & M. Ashayeri 10.1016/j.buildenv.2019.106270
19. Evaluation of simplified building energy models for urban-scale energy analysis of buildings F. Johari et al. 10.1016/j.buildenv.2021.108684
20. Design and Implementation of a 4D Web Application for Analytical Visualization of Smart City Applications S. Murshed et al. 10.3390/ijgi7070276
21. Comparison of building modelling assumptions and methods for urban scale heat demand forecasting D. Monien et al. 10.1186/s40984-017-0025-7