Urban Data and Building Energy Modeling: A GIS-Based Urban Building Energy Modeling System Using the Urban-EPC Engine

  • Chapter
  • First Online:
Planning Support Systems and Smart Cities

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

Abstract

There is a lack of building energy modeling in current planning support systems (PSS) while building energy efficiency is getting greater attention. This is due to the current limitations of energy modeling at the urban scale and the inconsistency between the available urban data and that required for modeling. The chapter seeks to fill this gap by develo** a GIS-based urban building energy modeling system, using the Urban-EPC simulation engine, a modified Energy Performance Calculator engine. This modeling system is compatible with other planning tools, enhanced by the combination of physical and statistical modeling, and adjustable in its resolution, speed and accuracy. Through processing the Data Preparation, Pre-Simulation, Main Simulation and Visualization and Analysis models in this energy modeling system, the urban data related to the basic building information, mutual shading, microclimate and occupant behavior are collected, modified, and synthesized in the GIS platform and then used as the input of the Urban-EPC engine to get energy use of every building in a city, which could be further visualized and analyzed. The method is applied in Manhattan to show its potential as an important component in PSS to inform urban energy policy making.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (France)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 117.69
Price includes VAT (France)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 158.24
Price includes VAT (France)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 158.24
Price includes VAT (France)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Ackoff, R. L. (1971). Towards a system of systems concepts. Management Science, 17(11), 661–671.

    Article  Google Scholar 

  • Al-Homoud, M. S. (2001). Computer-aided building energy analysis techniques. Building and Environment, 36(4), 421–433. doi:10.1016/S0360-1323(00)00026-3.

    Article  Google Scholar 

  • American Society of Heating, Refrigerating Air-Conditioning Engineers, & Illuminating Engineering Society of North America. (1989). Energy efficient design of new buildings except low-rise residential buildings: ASHRAE/IESNA Standard 90.1-2004. American Society of Heating, Refrigerating Air-Conditioning Engineers. Atlanta, GA.

    Google Scholar 

  • American Society of Heating, Refrigerating Air-Conditioning Engineers, & Illuminating Engineering Society of North America. (2004). Energy efficient design of new buildings except low-rise residential buildings: ASHRAE/IESNA Standard 90.1-2004. American Society of Heating, Refrigerating Air-Conditioning Engineers. Atlanta, GA.

    Google Scholar 

  • Batty, M. (2013). The new science of cities. Cambridge, MA: The Mit Press.

    Google Scholar 

  • Booth, A. T., Choudhary, R., & Spiegelhalter, D. J. (2012). Handling uncertainty in housing stock models. Building and Environment, 48, 35–47. doi:10.1016/j.buildenv.2011.08.016.

    Article  Google Scholar 

  • Branco, G., Lachal, B., Gallinelli, P., & Weber, W. (2004). Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data. Energy and Buildings, 36(6), 543–555.

    Article  Google Scholar 

  • Crawley, D. B., Lawrie, L. K., Winkelmann, F. C., Buhl, W. F., Huang, Y. J., Pedersen, C. O., et al. (2001). EnergyPlus: creating a new-generation building energy simulation program. Energy and Buildings, 33(4), 319–331.

    Article  Google Scholar 

  • Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., Torcellini, P., et. al. (2011). US Department of Energy commercial reference building models of the national building stock. National Renewable Energy Laboratory. Retrieved from http://www.nrel.gov/docs/fy11osti/46861.pdf.

  • Dodge Data and Analytics. (2005). McGraw Hill construction. Retrieved from: http://construction.com/dodge/.

  • Döllner, J., & Hagedorn, B. (2007). Integrating urban GIS, CAD, and BIM data by service based virtual 3D city models. In M. Rumor, V. Coors, E. M. Fendel, & S. Zlatanova (Eds.), Urban and regional data management-annual (pp. 157–160). London: Taylor & Francis Group.

    Google Scholar 

  • Eliasson, I. (2000). The use of climate knowledge in urban planning. Landscape and Urban Planning, 48(1), 31–44.

    Article  Google Scholar 

  • Esri. (2012). What is ArcPy? ArcGIS Help 10.1. Retrieved from http://resources.arcgis.com/en/help/main/10.1/index.html#//000v000000v7000000. Accessed December 1 2014.

  • Flaxman, M. (2010). Fundamentals of Geodesign. In E. Buhmann, M. Pietsch, & E. Kretzler (Eds.), Peer reviewed proceedings of digital landscape architecture 2010 at Anhalt university of applied sciences (pp. 28–41). Heidelberg, Germany: Wichmann Verlag.

    Google Scholar 

  • Golany, G. S. (1996). Urban design morphology and thermal performance. Atmospheric Environment, 30(3), 455–465.

    Article  Google Scholar 

  • Guerra Santin, O., Itard, L., & Visscher, H. (2009). The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock. Energy and Buildings, 41(11), 1223–1232.

    Article  Google Scholar 

  • Harris, B., & Batty, M. (1993). Locational models, geographic information and planning support systems. Journal of Planning Education and Research, 12(3), 184–198.

    Article  Google Scholar 

  • Hassid, S., Santamouris, M., Papanikolaou, N., Linardi, A., Klitsikas, N., Georgakis, C., et al. (2000). The effect of the Athens heat island on air conditioning load. Energy and Buildings, 32(2), 131–141.

    Article  Google Scholar 

  • Hogeling, J., & Van Dijk, D. (2008). P60 More information on the set of CEN standards for the EPBD. European Communities. Retrieved from http://www.buildup.eu/sites/default/files/P060_EN_EPBD_CEN_March2008_p3031.pdf.

  • Integrated Environmental Solutions Limited. (2012). VE-Ware. Retrieved from http://www.iesve.com/software/ve-ware.

  • International Organization for Standardization. (2008). CEN-ISO Standard 13790-2008: Energy performance of buildings—Calculation of energy use for space heating and cooling. International Organization for Standardization. Retrieved from http://www.iso.org/iso/catalogue_detail.htm%3Fcsnumber=41974.

  • Kim, J.-H., Augenbroe, G., & Suh, H.-S. (2013). Comparative study of the leed and ISO-CEN building energy performance rating methods. In E. Wurtz (Ed.), Building simulation 2013: Proceedings of BS2013: 13th conference of IBPSA (International Building Performance Association) (pp. 3104–3111). France: International Building Performance Simulation Association.

    Google Scholar 

  • Klosterman, R. E. (2008). A New Tool for a New Planning: The What if?TM Planning Support System. In R. K. Brail (Ed.), Planning support systems for cities and regions. Hampshire: Puritan Press Incorporated.

    Google Scholar 

  • Kolokotroni, M., Giannitsaris, I., & Watkins, R. (2006). The effect of the London urban heat island on building summer cooling demand and night ventilation strategies. Solar Energy, 80(4), 383–392.

    Article  Google Scholar 

  • Lee, S. H., Zhao, F., & Augenbroe, G. (2013). The use of normative energy calculation beyond building performance rating. Journal of Building Performance Simulation, 6(4), 282–292.

    Article  Google Scholar 

  • Littlefair, P. (1998). Passive solar urban design: ensuring the penetration of solar energy into the city. Renewable and Sustainable Energy Reviews, 2(3), 303–326.

    Article  Google Scholar 

  • MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations (Vol. 1: Statistics, pp. 281–297). Berkeley, CA: University of California Press.

    Google Scholar 

  • Maier, M. W. (1998). Architecting principles for systems-of-systems. Systems Engineering, 1(4), 267–284.

    Article  Google Scholar 

  • McPherson, E. G., & Simpson, J. R. (2003). Potential energy savings in buildings by an urban tree planting programme in California. Urban Forestry & Urban Greening, 2(2), 73–86.

    Article  Google Scholar 

  • Mitchell, G. (2005). Urban development, form and energy use in buildings: A review for the solutions project. EPSRC SUE SOLUTIONS Consortium. School of Geography and Institute for Transport Studies, University of Leeds. Retrieved from http://web.mit.edu/cron/Backup/project/urban_metabolism/TGOFF/readings%20and%20websites/Urban%2520development,%2520form%2520and%2520energy%2520use%2520in%2520buildings.pdf.

  • Mohammadi, S., de Vries, B., & Schaefer, W. (2013). A comprehensive review of existing urban energy models in the built environment. In S. Geertman, F. Toppen, & J. Stillwell (Eds.), Planning support systems for sustainable urban development (pp. 249–265). Heidelberg: Springer.

    Chapter  Google Scholar 

  • NYC Department of City Planning. (2014). BYTES of the BIG APPLE. Available from NYC Department of City Planning, http://www.nyc.gov/html/dcp/html/bytes/applbyte.shtml.

  • NYC Department of Information Technology & Telecommunications. (2014). Building footprints GIS file. Available from NYC Department of Information Technology & Telecommunications, https://nycopendata.socrata.com/.

  • Ok, V. (1992). A procedure for calculating cooling load due to solar radiation: The shading effects from adjacent or nearby buildings. Energy and Buildings, 19(1), 11–20.

    Article  Google Scholar 

  • Oke, T., Johnson, G., Steyn, D., & Watson, I. (1991). Simulation of surface urban heat islands under ‘ideal’ conditions at night part 2: Diagnosis of causation. Boundary-Layer Meteorology, 56(4), 339–358.

    Article  Google Scholar 

  • Perez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394–398. doi:10.1016/j.enbuild.2007.03.007.

    Article  Google Scholar 

  • Pisello, A. L., Taylor, J. E., Xu, X. Q., & Cotana, F. (2012). Inter-building effect: Simulating the impact of a network of buildings on the accuracy of building energy performance predictions. Building and Environment, 58, 37–45. doi:10.1016/j.buildenv.2012.06.017.

    Article  Google Scholar 

  • Quan, S. J., Economou, A., Grasl, T., & Yang, P. P.-J. (2014). Computing energy performance of building density, shape and typology in urban context. Energy Procedia, 61, 1602–1605.

    Article  Google Scholar 

  • Quan, S. J., Minter, J. D., & Yang, P. P.-J. (2013). A GIS-based performance metrics for designing a low energy urban agriculture system. In S. Geertman, F. Toppen, & J. Stillwell (Eds.), Planning support systems for sustainable urban development (pp. 225–247). Heidelberg: Springer.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ratti, C., & Richens, P. (2004). Raster analysis of urban form. Environment and Planning B-Planning and Design, 31(2), 297–309.

    Article  Google Scholar 

  • Reades, J., Calabrese, F., Sevtsuk, A., & Ratti, C. (2007). Cellular census: Explorations in urban data collection. Pervasive Computing, IEEE, 6(3), 30–38.

    Article  Google Scholar 

  • Reinhart, C., Dogan, T., Jakubiec, J. A., Rakha, T., & Sang, A. (2013). Umi-an urban simulation environment for building energy use, daylighting and walkability. In E. Wurtz (Ed.), Building simulation 2013: Proceedings of BS2013: 13th conference of IBPSA (International Building Performance Association) (pp. 476–483). France: International Building Performance Simulation Association.

    Google Scholar 

  • Robinson, D., Haldi, F., Kämpf, J., Leroux, P., Perez, D., Rasheed, A., & Wilke, U. (2009). CitySim: Comprehensive micro-simulation of resource flows for sustainable urban planning. In E. Wurtz (Ed.), Building Simulation 2009: Proceedings of BS2013: 11th Conference of IBPSA (International Building Performance Association) (pp. 1083–1090). Scotland: International Building Performance Simulation Association.

    Google Scholar 

  • Rode, P., Keim, C., Robazza, G., Viejo, P., & Schofield, J. (2013). Cities and energy: urban morphology and residential heat-energy demand. Environment and Planning B: Planning and Design, 40(2013).

    Google Scholar 

  • Santamouris, M., Papanikolaou, N., Livada, I., Koronakis, I., Georgakis, C., Argiriou, A., & Assimakopoulos, D. N. (2001). On the impact of urban climate on the energy consumption of buildings. Solar Energy, 70(3), 201–216. doi:10.1016/S0038-092x(00)00095-5.

    Article  Google Scholar 

  • Snyder, K. (2003). Tools for community design and decision-making. In S. Geertman & J. Stillwell (Eds.), Planning support systems in practice (pp. 99–120). Berlin: Springer.

    Chapter  Google Scholar 

  • Steadman, P. (1979). Energy and patterns of land use. In D. Watson (Ed.), Energy conservation through building design (pp. 246–260). New York: McGraw-Hill.

    Google Scholar 

  • Steemers, K. (2003). Energy and the city: Density, buildings and transport. Energy and Buildings, 35(1), 3–14. doi:10.1016/S0378-7788(02)00075-0.

    Article  Google Scholar 

  • SunEarthTools.com. (2014). Annual sun path. Retrieved from http://www.sunearthtools.com/dp/tools/pos_sun.php, Accessed December 1 2014.

  • The City of New York. (2014). About LL84. Retrieved from http://www.nyc.gov/html/gbee/html/plan/ll84_about.shtml, Accessed December 1 2014.

  • U.S. Energy Information Administration. (2005). 2003 CBECS (commercial buildings energy consumption survey) survey data. Retrieved from http://www.eia.gov/consumption/commercial/data/2003/index.cfm?view=consumption.

  • United States Census Bureau. (2014a). LEHD origin-destination employment statistics (LODES). Retrieved from: http://lehd.ces.census.gov/data/.

  • United States Census Bureau. (2014b). TIGER Products. Retrieved from: https://www.census.gov/geo/maps-data/data/tiger.html.

  • Wong, N. H., Jusuf, S. K., Syafii, N. I., Chen, Y. X., Hajadi, N., Sathyanarayanan, H., et al. (2011). Evaluation of the impact of the surrounding urban morphology on building energy consumption. Solar Energy, 85(1), 57–71. doi:10.1016/j.solener.2010.11.002.

    Article  Google Scholar 

  • Yeo, I., Yoon, S.-H., & Yee, J.-J. (2013). Development of an Environment and energy Geographical Information System (E-GIS) construction model to support environmentally friendly urban planning. Applied Energy, 104, 723–739.

    Article  Google Scholar 

  • Yezioro, A., & Shaviv, E. (1994). Shading: a design tool for analyzing mutual shading between buildings. Solar Energy, 52(1), 27–37.

    Article  Google Scholar 

  • Yi, Y. K., & Malkawi, A. M. (2009). Optimizing building form for energy performance based on hierarchical geometry relation. Automation in Construction, 18(6), 825–833. doi:10.1016/j.autcon.2009.03.006.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Perry Pei-Ju Yang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Quan, S.J., Li, Q., Augenbroe, G., Brown, J., Yang, P.PJ. (2015). Urban Data and Building Energy Modeling: A GIS-Based Urban Building Energy Modeling System Using the Urban-EPC Engine. In: Geertman, S., Ferreira, Jr., J., Goodspeed, R., Stillwell, J. (eds) Planning Support Systems and Smart Cities. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-18368-8_24

Download citation

Publish with us

Policies and ethics

Navigation