Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F271

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 317–326 (2017)

Full text

Abstract. In the context of European climate goals municipalities have an increasing interest in an accurate estimation of current and future energy demand in buildings, as the domestic energy consumption is one of the major adjusting screws for the reduction of electrical and thermal energy consumption, whereas the demand for space heating has the highest impact. As part of the ongoing GEWISS project it is planned to create a geographical information system (GIS) to visualize domestic and industrial heat consumption in the city of Hamburg (Germany) to support political decision making by linking the development of urban areas and the district heating grid. Additionally, it is planned to provide simulation capabilities to offer planning assistance for future development. This paper will present the underlying agent-based simulation system that is used to simulate the development of the building stock. Thereby, the simulation approach and first results regarding the development of the renovation state of the building stock based on a study about the renovation behavior of different types of home-owners of detached and terraced houses will be presented.

References

1. T. Klaus, C. Vollmer, K. Weber, H. Lehmann, and K. Müschen, “Energy target 2050: 100% renewable electricity supply,” Federal Environment Agency Germany, Tech. Rep., 2010.
2. S. Musa, “Smart cities - a roadmap for development,” Journal of Telecommunications System and Management, vol. 5, no. 144, 2016. http://dx.doi.org/10.4172/2167-0919.1000144. [Online]. Available: https://www.omicsgroup.org/journals/smart-cities--a-roadmap-for-development-2167-0919-1000144.pdf
3. S. Ackmann, I. Dochev, D. Hering, M. Gottschick, L. Knopp, S. Ochse, I. Peters, T. Preisler, W. Renz, and H. Seller, “GEWISS - Geographisches WärmeInformations- und SimulationsSystem,” in Kongress 2017: EnergieEffizienzBauen, 2017.
4. T. Preisler, T. Dethlefs, and W. Renz, “Simulation as a service: A design approach for large-scale energy network simulations,” in 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Sept 2015. http://dx.doi.org/10.15439/2015F116 pp. 1765–1772.
5. I. Stieß, V. van der Land, B. Birzle-Harder, and J. Deffner, “Handlungsmotive, -hemmnisse und Zielgruppen für eine energetische Gebäudesanierung - Ergebnisse einer standardisierten Befragung von Eigenheimsanierern,” Energieeffiziente Sanierung von Eigenheimen, resreport, 2010.
6. M. J. North, N. T. Collier, J. Ozik, E. R. Tatara, C. M. Macal, M. Bragen, and P. Sydelko, “Complex adaptive systems modeling with repast simphony,” Complex Adaptive Systems Modeling, vol. 1, no. 1, p. 3, 2013. http://dx.doi.org/10.1186/2194-3206-1-3. [Online]. Available: http://dx.doi.org/10.1186/2194-3206-1-3
7. I. Dochev, E. Muñoz, H. Seller, and I. Peters, “Assigning iwu building types to buildings in the hamburg alkis,” 2017.
8. E. Muñoz H., I. Dochev, H. Seller, and I. Peters, “Constructing a synthetic city for estimating spatially disaggregated heat demand,” International Journal of Microsimulation, vol. 9, no. 3, pp. 66–88, 2016. [Online]. Available: http://EconPapers.repec.org/RePEc:ijm:journl:v:9:y:2016:i:3:p:66-88
9. N. Schwarz, “The german microcensus,” Schmollers Jahrbuch, no. 121, pp. 649–654, 2001.
10. R. Nouvel, C. Schulte, U. Eicker, D. Pietruschka, and V. Coors, “Citygml-based 3d city model for energy diagnostics and urban energy policy support,” in Proceedings of the 13th conference of international Building Performance Simulation Association, 2013, pp. 218–25.
11. R. Nouvel, M. Zirak, H. Dastageeri, V. Coors, and U. Eicker, “Urban energy analysis based on 3d city model for national scale applications,” in Fifth German-Austrian IPBSA Conference, 2014.
12. P. Wate and V. Coors, “3d data models for urban energy simulation,” Energy Procedia, vol. 78, pp. 3372 – 3377, 2015. http://dx.doi.org/http://dx.doi.org/10.1016/j.egypro.2015.11.753. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1876610215024856
13. R. Nouvel, K. H. Brassel, M. Bruse, E. Duminil, V. Coors, U. Eicker, and D. Robinson, “Simstadt, a new workflow-driven urban energy simulation platform for citygml city models,” in Proceedings of the CISBAT International Conference 2015, 2015.
14. Open Geospatial Consortium, “Ogc city geography markup language (citygml) encoding standard,” 2012. [Online]. Available: https://portal.opengeospatial.org/files/?artifact_id=47842
15. P. Davidsson, “Multi agent based simulation: Beyond social simulation,” in Proceedings of the Second International Workshop on Multi-agent Based Simulation, ser. MABS 2000. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2001. ISBN 3-540-41522 pp. 97–107. [Online]. Available: http://dl.acm.org/citation.cfm?id=369837.369846
16. M. J. North, N. T. Collier, and J. R. Vos, “Experiences creating three implementations of the repast agent modeling toolkit,” ACM Trans. Model. Comput. Simul., vol. 16, no. 1, pp. 1–25, Jan. 2006. http://dx.doi.org/10.1145/1122012.1122013. [Online]. Available: http://doi.acm.org/10.1145/1122012.1122013
17. Environmental Systems Research Institute, “White paper: Esri shapefile technical description:,” Environmental Systems Research Institute, Inc., techreport, 1998. [Online]. Available: https://www.esri.com/library/whitepapers/pdfs/shapefile.pdf
18. A. Grignard, P. Taillandier, B. Gaudou, D. A. Vo, N. Q. Huynh, and A. Drogoul, GAMA 1.6: Advancing the Art of Complex Agent-Based Modeling and Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 117–131. ISBN 978-3-642-44927-7. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-44927-7_9
19. D.-A. Vo, A. Drogoul, J.-D. Zucker, and T.-V. Ho, A Modelling Language to Represent and Specify Emerging Structures in Agent-Based Model. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 212–227. ISBN 978-3-642-25920-3. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-25920-3_15
20. T. Loga, B. Stein, N. Diefenbach, and R. Born, “Deutsche Wohngebäudetypologie - Beispielhafte Maßnahmen zur Verbesserung der Energieeffizienz von typischen Wohngebäuden,” IWU - Institut Wohnen und Umwelt, Tech. Rep., 2015. [Online]. Available: http://www.building-typology.eu/downloads/public/docs/brochure/DE_TABULA_TypologyBrochure_IWU.pdf
21. “Richtlinienausschuss VDI 3807 Verbrauchskennwerte für Gebäude,” Verein Deutscher Ingenieure, Düsseldorf, DE, Standard, 1994.
22. T. Preisler, G. Balthasar, T. Dethlefs, and W. Renz, “Scalable integration of 4gl-models and algorithms for massive smart grid simulations and applications,” in 28th International Conference on Informatics for Environmental Protection: ICT for Energy Effieciency, EnviroInfo 2014, Oldenburg, Germany, September 10-12, 2014., J. M. Gómez, M. Sonnenschein, U. Vogel, A. Winter, B. Rapp, and N. Giesen, Eds. BIS-Verlag, 2014. ISBN 978-3-8142-2317-9 pp. 341–348. [Online]. Available: http://www.enviroinfo2014.org/
23. T. Preisler, T. Dethlefs, and W. Renz, “Data-adaptive simulation: Cooperativeness of users in bike-sharing systems,” in Proceedings of the Hamburg International Conference of Logistics, W. Kersten, T. Blecker, and C. M. Ringle, Eds., vol. 20. epubli GmbH, 2015.
24. U. Hunkeler, H. L. Truong, and A. Stanford-Clark, “Mqtt-s - a publish/subscribe protocol for wireless sensor networks,” in Communication Systems Software and Middleware and Workshops, 2008. COMSWARE 2008. 3rd International Conference on, Jan 2008. http://dx.doi.org/10.1109/COMSWA.2008.4554519 pp. 791–798.
25. A. Banks and R. Gupta, “Mqtt version 3.1. 1,” OASIS standard, 2014.