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Towards Integration of Spatial Context in Building Energy Demand Assessment Supported by CityGML Energy Extension

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Web and Wireless Geographical Information Systems (W2GIS 2023)

Abstract

The quality of Building Energy Models (BEMs), as dominant techniques to simulate and analyze building behavior in terms of energy consumption, depends strongly on the weather data that is generally captured by spatially low-resolution weather stations and in 2D. The provided weather data does not satisfy the BEMs requirements in terms of accuracy and spatial details. To address this issue, WSNs (Wireless Sensor Networks) have shown a high potential in offering 3D measurements with desired resolution and quality. However, the optimal deployment of a point-based wireless sensor network in an urban area to capture information on microclimate is a challenging task due to the complexity of the integration and management of diverse affecting factors as well as the 3D nature of the urban environment and its dynamics. This paper proposes to design and develop a workflow based on CityGML-standards to represent and manage the required spatiotemporal information for BEMs and feed a knowledgebase that can be used in WSN deployment optimization algorithms. Finally, the paper presents and discusses a case study to highlight the advantages and limitations of the proposed approach.

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Notes

  1. 1.

    A dataset that is designed to represent typical annual meteorological data.

References

  1. Eremia, M., Toma, L., Sanduleac, M.: The smart city concept in the 21st century. Procedia Eng. 181, 12–19 (2017). https://doi.org/10.1016/j.proeng.2017.02.357

    Article  Google Scholar 

  2. Zubizarreta, I., Seravalli, A., Arrizabalaga, S.: Smart city concept: what it is and what it should be. J. Urban Plan. Dev. 142, 04015005 (2016). https://doi.org/10.1061/(asce)up.1943-5444.0000282

    Article  Google Scholar 

  3. Kahsay, M.T., Bitsuamlak, G., Tariku, F.: Effect of localized exterior convective heat transfer on high-rise building energy consumption. Build. Simul. 13(1), 127–139 (2019). https://doi.org/10.1007/s12273-019-0568-7

    Article  Google Scholar 

  4. Bahu, J.-M., Koch, A., Kremers, E., Murshed, S.M.: Towards a 3D spatial urban energy modelling approach. Int. J. 3-D Inf. Model. 3, 1–16 (2015). https://doi.org/10.4018/ij3dim.2014070101

  5. Reinhart, C.F., Cerezo Davila, C.: Urban building energy modeling - A review of a nascent field. Build. Environ. 97, 196–202 (2016). https://doi.org/10.1016/j.buildenv.2015.12.001

  6. Agugiaro, G., Robineau, J.L., Rodrigues, P.: Project ci-nergy: towards an integrated energy urban planning system from a data modelling and system architecture perspective. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 4, 5–12 (2017). https://doi.org/10.5194/ISPRS-ANNALS-IV-4-W3-5-2017

  7. Nouvel, R., et al.: SimStadt, a new workflow-driven urban energy simulation platform for CityGML city models. In: Proceedings of CISBAT 2015 International Conference on Future Buildings and Districts - Sustainability from Nano to Urban Scale, pp. 889–894 (2015). https://doi.org/10.5075/EPFL-CISBAT2015-889-894

  8. Katal, A., Mortezazadeh, M., Wang, L. (Leon), Yu, H.: Urban building energy and microclimate modeling – From 3D city generation to dynamic simulations. Energy 251, 123817 (2022). https://doi.org/10.1016/J.ENERGY.2022.123817

  9. Moradi, A., Kavgic, M., Costanzo, V., Evola, G.: Impact of typical and actual weather years on the energy simulation of buildings with different construction features and under different climates. Energy 270, 126875 (2023). https://doi.org/10.1016/J.ENERGY.2023.126875

  10. Ma, Y.X., Yu, C.: Impact of meteorological factors on high-rise office building energy consumption in Hong Kong: From a spatiotemporal perspective. Energy Build. 228, 110468 (2020). https://doi.org/10.1016/j.enbuild.2020.110468

  11. 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. 9 (2020). https://doi.org/10.3390/IJGI9100602

  12. Lauzet, N., et al.: How building energy models take the local climate into account in an urban context – a review. Renew. Sustain. Energy Rev. 116, 109390 (2019). https://doi.org/10.1016/J.RSER.2019.109390

    Article  Google Scholar 

  13. Chalal, M.L., Benachir, M., White, M., Shrahily, R.: Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: a review. Renew. Sustain Energy Rev. 64, 761–776 (2016). https://doi.org/10.1016/j.rser.2016.06.040

  14. TRNSYS: Transient System Simulation Tool. http://www.Trnsys.Com/ (2013)

  15. U.S. Department of Energy: EnergyPlus|EnergyPlus (2020)

    Google Scholar 

  16. Emmanuel, W., Jérôme, K.: A verification of CitySim results using the BESTEST and monitored consumption values. In: Building Simulation Applications, pp. 215–222 (2015)

    Google Scholar 

  17. Reinhart, C.F., Dogan, T., Jakubiec, J.A., Rakha, T., Sang, A.: UMI - An urban simulation environment for building energy use, daylighting and walkability. In: Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association. pp. 476–483 (2013). https://doi.org/10.26868/25222708.2013.1404

  18. Bueno, B., Norford, L., Hidalgo, J., Pigeon, G.: The urban weather generator. J. Build. Perform. Simul. 6, 269–281 (2013). https://doi.org/10.1080/19401493.2012.718797

    Article  Google Scholar 

  19. Moradi, M., et al.: The vertical city weather generator (vcwg v1.3.2). Geosci. Model Dev. 14, 961–984 (2021). https://doi.org/10.5194/gmd-14-961-2021

  20. Huang, J., Jones, P., Zhang, A., Peng, R., Li, X., Chan, P.: Urban Building Energy and Climate (UrBEC) simulation: Example application and field evaluation in Sai Ying Pun. Hong Kong. Energy Build. 207, 109580 (2020). https://doi.org/10.1016/j.enbuild.2019.109580

    Article  Google Scholar 

  21. Gracik, S., Heidarinejad, M., Liu, J., Srebric, J.: Effect of urban neighborhoods on the performance of building cooling systems. Build. Environ. 90, 15–29 (2015). https://doi.org/10.1016/J.BUILDENV.2015.02.037

    Article  Google Scholar 

  22. Yao, R., Luo, Q., Li, B.: A simplified mathematical model for urban microclimate simulation. Build. Environ. 46, 253–265 (2011). https://doi.org/10.1016/j.buildenv.2010.07.019

    Article  Google Scholar 

  23. Liang, W., Huang, J., Jones, P., Wang, Q., Hang, J.: A zonal model for assessing street canyon air temperature of high-density cities. Build. Environ. 132, 160–169 (2018). https://doi.org/10.1016/J.BUILDENV.2018.01.035

    Article  Google Scholar 

  24. Huang, J., Jones, P., Zhang, A., Hou, S.S., Hang, J., Spengler, J.D.: Outdoor airborne transmission of coronavirus among apartments in high-density cities. Front. Built Environ. 7, 48 (2021). https://doi.org/10.3389/FBUIL.2021.666923

    Article  Google Scholar 

  25. Rodler, A., et al.: Urban microclimate and building energy simulation coupling techniques. In: Palme, M., Salvati, A. (eds.) Urban Microclimate Modelling for Comfort and Energy Studies, pp. 317–337. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-65421-4_15

    Chapter  Google Scholar 

  26. Katal, A., Mortezazadeh, M., Wang, L.: (Leon): Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations. Appl. Energy. 250, 1402–1417 (2019). https://doi.org/10.1016/j.apenergy.2019.04.192

    Article  Google Scholar 

  27. Agugiaro, G., Benner, J., Cipriano, P., Nouvel, R.: The energy application domain extension for CityGML: enhancing interoperability for urban energy simulations. Open Geospatial Data, Softw. Stand. 3(1), 1–30 (2018). https://doi.org/10.1186/s40965-018-0042-y

    Article  Google Scholar 

  28. Soilán, M., Truong-Hong, L., Riveiro, B., Laefer, D.: Automatic extraction of road features in urban environments using dense ALS data. Int. J. Appl. Earth Obs. Geoinf. 64, 226–236 (2018). https://doi.org/10.1016/j.jag.2017.09.010

    Article  Google Scholar 

  29. Rosser, J.F., Long, G., Zakhary, S., Boyd, D.S., Mao, Y., Robinson, D.: Modelling urban housing stocks for building energy simulation using CityGML energyade. ISPRS Int. J. Geo-Inf. 8, 163 (2019). https://doi.org/10.3390/ijgi8040163

    Article  Google Scholar 

  30. Malhotra, A., Shamovich, M., Frisch, J., van Treeck, C.: Urban energy simulations using open CityGML models: a comparative analysis. Energy Build. 255, 111658 (2022). https://doi.org/10.1016/J.ENBUILD.2021.111658

    Article  Google Scholar 

  31. Wang, X.: Using CityGML EnergyADE Data in Honeybee (2020)

    Google Scholar 

  32. Argany, M., Mostafavi, M.A., Gagné, C.: Context-aware local optimization of sensor network deployment. J. Sens. Actuator Netw. 4, 160–188 (2015). https://doi.org/10.3390/jsan4030160

    Article  Google Scholar 

  33. Sailor, D.J., Georgescu, M., Milne, J.M., Hart, M.A.: Development of a national anthropogenic heating database with an extrapolation for international cities. Atmos. Environ. 118, 7–18 (2015). https://doi.org/10.1016/J.ATMOSENV.2015.07.016

    Article  Google Scholar 

  34. Yao, Z., et al.: 3DCityDB - a 3D geodatabase solution for the management, analysis, and visualization of semantic 3D city models based on CityGML. Open Geospatial Data, Softw. Stand. 3(1), 1–26 (2018). https://doi.org/10.1186/s40965-018-0046-7

    Article  MathSciNet  Google Scholar 

  35. Cao, J., Zhou, W., Zheng, Z., Ren, T., Wang, W.: Within-city spatial and temporal heterogeneity of air temperature and its relationship with land surface temperature. Landsc. Urban Plan. 206, 103979 (2021). https://doi.org/10.1016/j.landurbplan.2020.103979

    Article  Google Scholar 

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Doodman, S., Mostafavi, M.A., Sengupta, R. (2023). Towards Integration of Spatial Context in Building Energy Demand Assessment Supported by CityGML Energy Extension. In: Mostafavi, M.A., Del Mondo, G. (eds) Web and Wireless Geographical Information Systems. W2GIS 2023. Lecture Notes in Computer Science, vol 13912. Springer, Cham. https://doi.org/10.1007/978-3-031-34612-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-34612-5_2

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