URBAN ENERGY MODELLING APPROACHES: A LITERATURE REVIEW

Yıl 2019, Cilt: 4 Sayı: 2, 57 - 64, 27.05.2020

Ayse Zelal Tugrul

Öz

The energy efficiency of buildings at district and neighborhood level are limited availability, however there are multiple ways followed to evaluate the energy performance at the urban scale. The current methodologies on the energy efficiency strategies for future cities follow different analysis steps and require different level of detail of informations depending on the project. The aim of this paper to provide a review on energy modelling approaches. The two main approaches are identified in this paper: top-down and bottom-up. The top-down approach is a deductive method which analyses the information by diving them into the building stocks. The bottom-up approach is an inductive method that is based on the calculation of individual level of energy consumption then the total sum of them to represent a region. The review is based on observations from the case studies following the top-down and bottom-up approaches.

Anahtar Kelimeler

Urban modelling, Bottom-up, Top-down, Building energy consumption

Kaynakça

• [1] C. Directive, “Directive 2010/75/EU of the European Parliament and of the Council,” Off. J. Eur. Union L, vol. 334, pp. 17–119, 2010.
• [2] C. S. Monteiro, A. Pina, C. Cerezo, C. Reinhart, and P. Ferrão, “The use of multi-detail building archetypes in urban energy modelling,” Energy Procedia, vol. 111, pp. 817–825, 2017.
• [3] E. Bernardi, S. Carlucci, C. Cornaro, and R. A. Bohne, “An analysis of the most adopted rating systems for assessing the environmental impact of buildings,” Sustainability, vol. 9, no. 7, p. 1226, 2017.
• [4] W. Chung, Y. V. Hui, and Y. M. Lam, “Benchmarking the energy efficiency of commercial buildings,” Applied Energy, vol. 83, no. 1, pp. 1–14, Jan. 2006, doi: 10.1016/j.apenergy.2004.11.003.
• [5] S.-M. Hong, G. Paterson, E. Burman, P. Steadman, and D. Mumovic, “A comparative study of benchmarking approaches for non-domestic buildings: Part 1–Top-down approach,” International Journal of Sustainable Built Environment, vol. 2, no. 2, pp. 119–130, 2013.
• [6] C. F. Reinhart and C. Cerezo Davila, “Urban building energy modeling – A review of a nascent field,” Building and Environment, vol. 97, pp. 196–202, Feb. 2016, doi: 10.1016/j.buildenv.2015.12.001.
• [7] L. G. Swan and V. I. Ugursal, “Modeling of end-use energy consumption in the residential sector: A review of modeling techniques,” Renewable and sustainable energy reviews, vol. 13, no. 8, pp. 1819–1835, 2009.
• [8] C. Filippın, “Benchmarking the energy efficiency and greenhouse gases emissions of school buildings in central Argentina,” Building and Environment, vol. 35, no. 5, pp. 407–414, 2000.
• [9] W.-S. Lee, “Benchmarking the energy efficiency of government buildings with data envelopment analysis,” Energy and Buildings, vol. 40, no. 5, pp. 891–895, 2008.
• [10] “ODYSSEE MURE, [Online], https://www.odyssee-mure.eu - Google Search.” [Online]. Available: https://www.google.com/search?q=ODYSSEE+MURE%2C+%5BOnline%5D%2C+https%3A%2F%2Fwww.odyssee-mure.eu&oq=ODYSSEE+MURE%2C+%5BOnline%5D%2C+https%3A%2F%2Fwww.odyssee-mure.eu&aqs=chrome..69i57j69i60.382j0j4&sourceid=chrome&ie=UTF-8. [Accessed: 30-Jan-2020].
• [11] A. J. Summerfield, R. J. Lowe, and T. Oreszczyn, “Two models for benchmarking UK domestic delivered energy,” Building Research & Information, vol. 38, no. 1, pp. 12–24, 2010.
• [12] J. Tornberg and L. Thuvander, “A GIS energy model for the building stock of Goteborg,” in ESRI International User Conference Proceedings, 2005.
• [13] G. P. Saha and J. Stephenson, “A model of residential energy use in New Zealand,” Energy, vol. 5, no. 2, pp. 167–175, Feb. 1980, doi: 10.1016/0360-5442(80)90005-5.
• [14] S. Heiple and D. J. Sailor, “Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles,” Energy and buildings, vol. 40, no. 8, pp. 1426–1436, 2008.
• [15] D. J. Sailor and L. Lu, “A top–down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areas,” Atmospheric environment, vol. 38, no. 17, pp. 2737–2748, 2004.
• [16] A. Mastrucci, O. Baume, F. Stazi, and U. Leopold, “Estimating energy savings for the residential building stock of an entire city: A GIS-based statistical downscaling approach applied to Rotterdam,” Energy and Buildings, vol. 75, pp. 358–367, 2014.
• [17] C. Protopapadaki, G. Reynders, and D. Saelens, “Bottom-up modelling of the Belgian residential building stock: impact of building stock descriptions,” in Proceedings of the 9th International Conference on System Simulation in Buildings-SSB2014, 2014.
• [18] M. Beccali, M. Cellura, M. Fontana, S. Longo, and M. Mistretta, “Energy retrofit of a single-family house: Life cycle net energy saving and environmental benefits,” Renewable and Sustainable Energy Reviews, vol. 27, pp. 283–293, 2013.
• [19] A. Mastrucci, E. Popovici, A. Marvuglia, L. De Sousa, E. Benetto, and U. Leopold, “GIS-based Life Cycle Assessment of urban building stocks retrofitting-a bottom-up framework applied to Luxembourg,” in EnviroInfo and ICT for Sustainability 2015, 2015.
• [20] J. A. Fonseca and A. Schlueter, “Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts,” Applied Energy, vol. 142, pp. 247–265, 2015.
• [21] “Urban Modeling Interface | MIT Sustainable Design Lab.” [Online]. Available: http://web.mit.edu/sustainabledesignlab/projects/umi/index.html. [Accessed: 30-Jan-2020].
• [22] D. Wang, J. Landolt, G. Mavromatidis, K. Orehounig, and J. Carmeliet, “CESAR: A bottom-up building stock modelling tool for Switzerland to address sustainable energy transformation strategies,” Energy and Buildings, vol. 169, pp. 9–26, Jun. 2018, doi: 10.1016/j.enbuild.2018.03.020.
• [23] A. S. Manne, A. S. Manne, R. G. Richels, and R. G. Richels, Buying greenhouse insurance: the economic costs of carbon dioxide emission limits. MIT press, 1992.
• [24] A. S. Manne and C.-O. Wene, “MARKAL-MACRO: A linked model for energy-economy analysis,” Brookhaven National Lab., Upton, NY (United States), 1992.
• [25] H. Klinge Jacobsen, “Integrating the bottom-up and top-down approach to energy–economy modelling: the case of Denmark,” Energy Economics, vol. 20, no. 4, pp. 443–461, Sep. 1998, doi: 10.1016/S0140-9883(98)00002-4.