Contained in:
Book Chapter

Solar Potential and Energy Assessment Data in U-BEM Models: Interoperability Analysis Between Performance Simulation Tools and OpenBIM/GIS Platforms

  • CARLO ZANCHETTA
  • Martina Giorio
  • Maria Grazia Donatiello
  • Federico Rossi
  • Rossana Paparella

To evaluate the energy and solar potential of the building stock and address feasibility studies of building retrofit interventions information standards are required to ensure proper data flow from building and urban models to simulation environments. Energy performance data are gathered from different information containers and therefore the result of simulations needs to be shared in BIM/GIS environments to better address energy policies and decision-making processes. Solar potential and energy retrofit estimation, developed by means of urban models (U-BEM) are too rough to support a decision-making process, even if at a feasibility stage. On the opposite, strategic decisions are defined with reference to large building stocks that require a U-BEM approach. To increase the reliability of this kind of simulations the study proposes to integrate U-BEMS with BIM-based data that are aggregated and published at urban scale as average performance indicators of built systems. The interoperability problem is analyzed both for simulation tools that need to manage this kind of data and openBIM/GIS platforms that need to share performance indicators and simulation results

  • Keywords:
  • Energy potential,
  • Solar potential,
  • IFC,
  • BIM,
  • U-BEM,
+ Show More

CARLO ZANCHETTA

University of Padua, Italy - ORCID: 0000-0002-8185-4301

Martina Giorio

University of Padua, Italy - ORCID: 0009-0005-7521-901X

Maria Grazia Donatiello

University of Padua, Italy

Federico Rossi

University of Padua, Italy

Rossana Paparella

University of Padua, Italy - ORCID: 0000-0002-5989-995X

  1. Amado, M., & Poggi, F. (2012). Towards solar urban planning: A new step for better energy performance. Energy Procedia, 30. DOI: 10.1016/j.egypro.2012.11.139
  2. Assouline, D., Mohajeri, N., & Scartezzini, J. L. (2017). Quantifying rooftop photovoltaic solar energy potential: A machine learning approach. Solar Energy, 141, 278–296. DOI: 10.1016/j.solener.2016.11.045
  3. Bahu, J. M., Koch, A., Kremers, E., & Murshed, S. M. (2013). Towards a 3D spatial urban energy modelling approach. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(2W1), 33–41. DOI: 10.5194/isprsannals-II-2-W1-33-2013
  4. Behr, F.-J. 1957-, & AGSE. 5 2012 Stuttgart. (2012). Geoinformation - catalyst for planning, development and good governance. AGSE Publishing.
  5. Boriani, M., Giambruno, M., & Garzulino, A. (2011). Studio, sviluppo e definizione di schede tecniche di intervento per l’efficienza energetica negli edifici di pregio.
  6. Borkowska, S., & Pokonieczny, K. (2022). Analysis of OpenStreetMap Data Quality for Selected Counties in Poland in Terms of Sustainable Development. Sustainability (Switzerland), 14(7). DOI: 10.3390/su14073728
  7. Bshouty, E., Shafir, A., & Dalyot, S. (2020). Towards the generation of 3D OpenStreetMap building models from single contributed photographs. Computers, Environment and Urban Systems, 79. DOI: 10.1016/j.compenvurbsys.2019.101421
  8. Corrado, V., Ballarini, I., & Corgnati, S. P. (2014). Building Typology Brochure-Italy Fascicolo sulla Tipologia Edilizia Italiana nuova edizione.
  9. Diana, L. (2017). Metodo CRI_TRA: un metodo di valutazione comparativa  delle criticità e della trasformabilità edilizia del  patrimonio residenziale pubblico in Italia. https://www.researchgate.net/publication/317002914
  10. Dorn, H., Törnros, T., & Zipf, A. (2015). Quality evaluation of VGI using authoritative data-a comparison with land use data in southern Germany. ISPRS International Journal of Geo-Information, 4(3), 1657–1671. DOI: 10.3390/ijgi4031657
  11. Elwood, S., Goodchild, M. F., & Sui, D. Z. (2012). Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice. Annals of the Association of American Geographers, 102(3), 571–590. DOI: 10.1080/00045608.2011.595657
  12. EPW Map. (n.d.). Retrieved August 7, 2023, from https://www.ladybug.tools/epwmap/
  13. Esclapés, J., Ferreiro, I., Piera, J., & Teller, J. (2014). A method to evaluate the adaptability of photovoltaic energy on urban façades. Solar Energy, 105. DOI: 10.1016/j.solener.2014.03.012
  14. Freitas, S., Catita, C., Redweik, P., & Brito, M. C. (2015). Modelling solar potential in the urban environment: State-of-the-art review. In Renewable and Sustainable Energy Reviews (Vol. 41). DOI: 10.1016/j.rser.2014.08.060
  15. Giannelli, D., León-Sánchez, C., & Agugiaro, G. (2022). Comparison and evaluation of different gis software tools to estimate solar irradiation. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5(4), 275–282. DOI: 10.5194/isprs-Annals-V-4-2022-275-2022
  16. Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69(4), 211–221. DOI: 10.1007/s10708-007-9111-y
  17. Haklay, M., & Weber, P. (2008). openstreetMap: User-Generated street Maps. www.openstreetmap.
  18. Heipke, C. (2010). Crowdsourcing geospatial data. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 550–557. DOI: 10.1016/j.isprsjprs.2010.06.005
  19. Italy/PCN - OpenStreetMap Wiki. (n.d.). Retrieved August 7, 2023, from https://wiki.openstreetmap.org/wiki/Italy/PCN
  20. Jakica, N. (2018). State-of-the-art review of solar design tools and methods for assessing daylighting and solar potential for building-integrated photovoltaics. Renewable and Sustainable Energy Reviews, 81. DOI: 10.1016/j.rser.2017.05.080
  21. Kabir, E., Kumar, P., Kumar, S., Adelodun, A. A., & Kim, K. H. (2018). Solar energy: Potential and future prospects. Renewable and Sustainable Energy Reviews, 82(1), 894–900. DOI: 10.1016/j.rser.2017.09.094
  22. Lan, H., Gou, Z., & Hou, C. (2022). Understanding the relationship between urban morphology and solar potential in mixed-use neighborhoods using machine learning algorithms. Sustainable Cities and Society, 87. DOI: 10.1016/j.scs.2022.104225
  23. Lee, J. G., & Kang, M. (2015). Geospatial Big Data: Challenges and Opportunities. Big Data Research, 2(2), 74–81. DOI: 10.1016/j.bdr.2015.01.003
  24. Lobaccaro, G., Lisowska, M. M., Saretta, E., Bonomo, P., & Frontini, F. (2019). A methodological analysis approach to assess solar energy potential at the neighborhood scale. Energies, 12(18). DOI: 10.3390/en12183554
  25. Manni, M., Nocente, A., Kong, G., Skeie, K., Fan, H., & Lobaccaro, G. (2022). Solar energy digitalization at high latitudes: A model chain combining solar irradiation models, a LiDAR scanner, and high-detail 3D building model. Frontiers in Energy Research, 10. DOI: 10.3389/fenrg.2022.1082092
  26. Mazzarella, L., & Piterà, L. A. (n.d.). Efficienza Energetica attraverso la Diagnosi e il Servizio Energia negli Edifici Linee Guida.
  27. Minaei, M. (2020). Evolution, density and completeness of OpenStreetMap road networks in developing countries: The case of Iran. Applied Geography, 119. DOI: 10.1016/j.apgeog.2020.102246
  28. Neis, P., & Zielstra, D. (2014). Recent Developments and Future Trends in Volunteered Geographic Information Research: The Case of OpenStreetMap. Future Internet, 6(1), 76–106. DOI: 10.3390/fi6010076
  29. OpenStreetMap Statistics. (n.d.). Retrieved August 7, 2023, from https://planet.openstreetmap.org/statistics/data_stats.html
  30. Peronato, G., Rastogi, P., Rey, E., & Andersen, M. (2018). A toolkit for multi-scale mapping of the solar energy-generation potential of buildings in urban environments under uncertainty. Solar Energy, 173, 861–874. DOI: 10.1016/j.solener.2018.08.017
  31. Rana, S., & Joliveau, T. (2009). NeoGeography: An extension of mainstream geography for everyone made by everyone? In Journal of Location Based Services (Vol. 3, Issue 2, pp. 75–81). DOI: 10.1080/17489720903146824
  32. Ratti, C., Baker, N., & Steemers, K. (2005). Energy consumption and urban texture. Energy and Buildings, 37(7), 762–776. DOI: 10.1016/j.enbuild.2004.10.010
  33. See, L., Estima, J., Pődör, A., Jokar Arsanjiani, J., Laso Bayas, J.-C., & Vatseva, R. (2017). Sources of VGI for Mapping. In Mapping and the Citizen Sensor (pp. 13–35). Ubiquity Press. DOI: 10.5334/bbf.b
  34. Vargas-Munoz, J. E., Srivastava, S., Tuia, D., & Falcao, A. X. (2021). OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing. IEEE Geoscience and Remote Sensing Magazine, 9(1), 184–199. DOI: 10.1109/MGRS.2020.2994107
PDF
  • Publication Year: 2023
  • Pages: 1021-1032

XML
  • Publication Year: 2023

Chapter Information

Chapter Title

Solar Potential and Energy Assessment Data in U-BEM Models: Interoperability Analysis Between Performance Simulation Tools and OpenBIM/GIS Platforms

Authors

CARLO ZANCHETTA, Martina Giorio, Maria Grazia Donatiello, Federico Rossi, Rossana Paparella

DOI

10.36253/979-12-215-0289-3.102

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

40

Fulltext
downloads

46

Views

Export Citation

1,310

Open Access Books

in the Catalogue

1,977

Book Chapters

3,390,281

Fulltext
downloads

4,172

Authors

from 873 Research Institutions

of 64 Nations

63

scientific boards

from 340 Research Institutions

of 43 Nations

1,157

Referees

from 344 Research Institutions

of 37 Nations