Abstract
The estimation of building’s energy use at the urban scale is an increasingly popular and necessary approach to minimise the primary energy use and GHG emissions. However, this activity involves the collection and management of a huge amount of data, which is often not organised in a (re)usable way by neither collectivities nor researchers. This chapter details the difficulties related to the collection of data from multiple sources, regarding incompatibilities and incompleteness in particular, and introduces the advantages of a data management using well-structured databases or GIS instead of spreadsheets or specific input files. Some research fields of interest for this matter, such as GIS, spatio-temporal databases and ontologies are introduced, and a complete approach to create a solid data management solution is proposed. The methodology described leads to a sound basis for advanced simulation and analysis of energy flow in urban zones.
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References
Ahern, J.: Exergy method of energy systems analysis (1980)
Date, C.J., Darwen, H., Lorentzos, N.A.: Temporal Data and the Relational Model. Morgan Kaufmann Publishers, USA (2003)
Feigenbaum, L., Herman, I., Hongsermeier, T., Neumann, E., Stephens, S.: The semantic web in action. Scientific American Magazine 297(6), 90–97 (2007)
Filchakova, N., Robinson, D., Thalmann, P.: A model of whole-city housing stock and its temporal evolution. In: Proc. Building Simulation, Glasgow, UK (2009)
Forrester, J.: Urban dynamics. MIT Press (1969)
Gadsden, S., Rylatt, M., Lomas, K., Robinson, D.: Predicting the urban solar fraction: a methodology for energy advisers and planners based on GIS. Energy & Buildings 35(1), 37–48 (2003)
Girardin, L., Marechal, F., Dubuis, M., Calame-Darbellay, N., Favrat, D.: EnerGis: A geographical information based system for the evaluation of integrated energy conversion systems in urban areas. Energy (2009)
Gruber, T., et al.: Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human Computer Studies 43(5), 907–928 (1995)
Güting, R.: An introduction to spatial database systems. The VLDB Journal 3(4), 357–399 (1994)
Jensen, C., Snodgrass, R.: Temporal data management. IEEE Transactions on Knowledge and Data Engineering 11(1), 36–44 (2002)
Jones, P., Patterson, J., Lannon, S.: Modelling the built environment at an urban scale—Energy and health impacts in relation to housing. Landscape and Urban Planning 83(1), 39–49 (2007)
Linnhoff, B.: Pinch analysis: a state-of-the-art overview: Techno-economic analysis. Chemical Engineering Research & Design 71(5), 503–522 (1993)
Lior, N.: Thoughts about future power generation systems and the role of exergy analysis in their development. Energy Conversion and Management 43(9-12), 1187–1198 (2002)
Pelekis, N., Theodoulidis, B., Kopanakis, I., Theodoridis, Y.: Literature review of spatio-temporal database models. The Knowledge Engineering Review 19(03), 235–274 (2004)
Robinson, D. (ed.): Computer modelling for sustainable urban design. Earthscan Press, London (2011)
Robinson, D., Campbell, N., Gaiser, W., Kabel, K., Le-Mouel, A., Morel, N., Page, J., Stankovic, S., Stone, A.: SUNtool–a new modelling paradigm for simulating and optimising urban sustainability. Solar Energy 81(9), 1196–1211 (2007)
Robinson, D., Haldi, F., Kämpf, J., Leroux, P., Perez, D., Rasheed, A., Wilke, U.: CitySim: Comprehensive micro-simulation of resource flows for sustainable urban planning. In: Proc. Building Simulation (2009)
Sartori, I., Wachenfeldt, B., Hestnes, A.: Energy demand in the Norwegian building stock: Scenarios on potential reduction. Energy Policy 37(5), 1614–1627 (2009)
Shadbolt, N., Hall, W., Berners-Lee, T.: The semantic web revisited. IEEE Intelligent Systems 21(3), 96–101 (2006)
Shimoda, Y., Fujii, T., Morikawa, T., Mizuno, M.: Development of residential energy end-use simulation model at city scale. In: Proc. Building Simulation (2003)
Shorrock, L., Dunster, J.: The physically-based model BREHOMES and its use in deriving scenarios for the energy use and carbon dioxide emissions of the UK housing stock. Energy Policy 25(12), 1027–1037 (1997)
Sugihara, H., Komoto, J., Tsuji, K.: A multi-objective optimization model for determining urban energy systems under integrated energy service in a specific area. Electrical Engineering in Japan 147(3), 20–31 (2004)
Yamaguchi, Y., Shimoda, Y., Mizuno, M.: Development of district energy system simulation model based on detailed energy demand model. In: Proceeding of Eighth International IBPSA Conference, pp. 1443–1450 (2003)
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Perez, D., Robinson, D. (2012). Urban Energy Flow Modelling: A Data-Aware Approach. In: Arisona, S.M., Aschwanden, G., Halatsch, J., Wonka, P. (eds) Digital Urban Modeling and Simulation. Communications in Computer and Information Science, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29758-8_11
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DOI: https://doi.org/10.1007/978-3-642-29758-8_11
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