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Developing a common approach for classifying building stock energy models

Author

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  • Langevin, J.
  • Reyna, J.L.
  • Ebrahimigharehbaghi, S.
  • Sandberg, N.
  • Fennell, P.
  • Nägeli, C.
  • Laverge, J.
  • Delghust, M.
  • Mata, É.
  • Van Hove, M.
  • Webster, J.
  • Federico, F.
  • Jakob, M.
  • Camarasa, C.

Abstract

Buildings contribute 40% of global greenhouse gas emissions; therefore, strategies that can substantially reduce emissions from the building stock are key components of broader efforts to mitigate climate change and achieve sustainable development goals. Models that represent the energy use of the building stock at scale under various scenarios of technology deployment have become essential tools for the development and assessment of such strategies. Within the past decade, the capabilities of building stock energy models have improved considerably, while model transferability and sharing has increased. Given these advancements, a new scheme for classifying building stock energy models is needed to facilitate communication of modeling approaches and the handling of important model dimensions. In this article, we present a new building stock energy model classification framework that leverages international modeling expertise from the participants of the International Energy Agency's Annex 70 on Building Energy Epidemiology. Drawing from existing classification studies, we propose a multi-layer quadrant scheme that classifies modeling techniques by their design (top-down or bottom-up) and degree of transparency (black-box or white-box); hybrid techniques are also addressed. The quadrant scheme is unique from previous classification approaches in its non-hierarchical organization, coverage of and ability to incorporate emerging modeling techniques, and treatment of additional modeling dimensions. The new classification framework will be complemented by a reporting protocol and online registry of existing models as part of ongoing work in Annex 70 to increase the interpretability and utility of building stock energy models for energy policy making.

Suggested Citation

  • Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
  • Handle: RePEc:eee:rensus:v:133:y:2020:i:c:s1364032120305645
    DOI: 10.1016/j.rser.2020.110276
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    as
    1. Mark Jaccard, 2009. "Combining Top Down and Bottom Up in Energy Economy Models," Chapters, in: Joanne Evans & Lester C. Hunt (ed.), International Handbook on the Economics of Energy, chapter 13, Edward Elgar Publishing.
    2. Porse, Erik & Derenski, Joshua & Gustafson, Hannah & Elizabeth, Zoe & Pincetl, Stephanie, 2016. "Structural, geographic, and social factors in urban building energy use: Analysis of aggregated account-level consumption data in a megacity," Energy Policy, Elsevier, vol. 96(C), pages 179-192.
    3. Shi, Jingcheng & Chen, Wenying & Yin, Xiang, 2016. "Modelling building’s decarbonization with application of China TIMES model," Applied Energy, Elsevier, vol. 162(C), pages 1303-1312.
    4. Cayla, Jean-Michel & Maïzi, Nadia, 2015. "Integrating household behavior and heterogeneity into the TIMES-Households model," Applied Energy, Elsevier, vol. 139(C), pages 56-67.
    5. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    6. Labandeira, Xavier & Labeaga, José M. & López-Otero, Xiral, 2012. "Estimation of elasticity price of electricity with incomplete information," Energy Economics, Elsevier, vol. 34(3), pages 627-633.
    7. Filippini, Massimo & Hunt, Lester C., 2012. "US residential energy demand and energy efficiency: A stochastic demand frontier approach," Energy Economics, Elsevier, vol. 34(5), pages 1484-1491.
    8. Wei Zhou & Alice Moncaster & David M Reiner & Peter Guthrie, 2019. "Estimating Lifetimes and Stock Turnover Dynamics of Urban Residential Buildings in China," Sustainability, MDPI, vol. 11(13), pages 1-18, July.
    9. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    10. Dilaver, Zafer & Hunt, Lester C, 2011. "Modelling and forecasting Turkish residential electricity demand," Energy Policy, Elsevier, vol. 39(6), pages 3117-3127, June.
    11. Wang, Huan & Chen, Wenying & Shi, Jingcheng, 2018. "Low carbon transition of global building sector under 2- and 1.5-degree targets," Applied Energy, Elsevier, vol. 222(C), pages 148-157.
    12. Giraudet, Louis-Gaëtan & Guivarch, Céline & Quirion, Philippe, 2012. "Exploring the potential for energy conservation in French households through hybrid modeling," Energy Economics, Elsevier, vol. 34(2), pages 426-445.
    13. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    14. Azar, Elie & Nikolopoulou, Christina & Papadopoulos, Sokratis, 2016. "Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling," Applied Energy, Elsevier, vol. 183(C), pages 926-937.
    15. Sandels, C. & Widén, J. & Nordström, L., 2014. "Forecasting household consumer electricity load profiles with a combined physical and behavioral approach," Applied Energy, Elsevier, vol. 131(C), pages 267-278.
    16. Fazeli, Reza & Davidsdottir, Brynhildur & Hallgrimsson, Jonas Hlynur, 2016. "Residential energy demand for space heating in the Nordic countries: Accounting for interfuel substitution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1210-1226.
    17. Ballarini, Ilaria & Corgnati, Stefano Paolo & Corrado, Vincenzo, 2014. "Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project," Energy Policy, Elsevier, vol. 68(C), pages 273-284.
    18. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
    19. Lindberg, K.B. & Bakker, S.J. & Sartori, I., 2019. "Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts," Utilities Policy, Elsevier, vol. 58(C), pages 63-88.
    20. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    21. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    22. Feng, Y.Y. & Chen, S.Q. & Zhang, L.X., 2013. "System dynamics modeling for urban energy consumption and CO2 emissions: A case study of Beijing, China," Ecological Modelling, Elsevier, vol. 252(C), pages 44-52.
    23. Eoin Ó Broin & Jonas Nässén & Filip Johnsson, 2015. "The influence of price and non-price effects on demand for heating in the EU residential sector," Post-Print hal-01219278, HAL.
    24. Ó Broin, Eoin & Nässén, Jonas & Johnsson, Filip, 2015. "The influence of price and non-price effects on demand for heating in the EU residential sector," Energy, Elsevier, vol. 81(C), pages 146-158.
    25. Fournier, Eric D. & Federico, Felicia & Porse, Erik & Pincetl, Stephanie, 2019. "Effects of building size growth on residential energy efficiency and conservation in California," Applied Energy, Elsevier, vol. 240(C), pages 446-452.
    26. Millward-Hopkins, J.T. & Tomlin, A.S. & Ma, L. & Ingham, D.B. & Pourkashanian, M., 2013. "Assessing the potential of urban wind energy in a major UK city using an analytical model," Renewable Energy, Elsevier, vol. 60(C), pages 701-710.
    27. Lecca, Patrizio & McGregor, Peter G. & Swales, J. Kim & Turner, Karen, 2014. "The added value from a general equilibrium analysis of increased efficiency in household energy use," Ecological Economics, Elsevier, vol. 100(C), pages 51-62.
    28. Limpens, Gauthier & Moret, Stefano & Jeanmart, Hervé & Maréchal, Francois, 2019. "EnergyScope TD: A novel open-source model for regional energy systems," Applied Energy, Elsevier, vol. 255(C).
    29. Zhang, Wenwen & Robinson, Caleb & Guhathakurta, Subhrajit & Garikapati, Venu M. & Dilkina, Bistra & Brown, Marilyn A. & Pendyala, Ram M., 2018. "Estimating residential energy consumption in metropolitan areas: A microsimulation approach," Energy, Elsevier, vol. 155(C), pages 162-173.
    30. Hargreaves, Anthony & Cheng, Vicky & Deshmukh, Sandip & Leach, Matthew & Steemers, Koen, 2017. "Forecasting how residential urban form affects the regional carbon savings and costs of retrofitting and decentralized energy supply," Applied Energy, Elsevier, vol. 186(P3), pages 549-561.
    31. Wilkerson, Jordan T. & Cullenward, Danny & Davidian, Danielle & Weyant, John P., 2013. "End use technology choice in the National Energy Modeling System (NEMS): An analysis of the residential and commercial building sectors," Energy Economics, Elsevier, vol. 40(C), pages 773-784.
    32. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    33. Fumo, Nelson, 2014. "A review on the basics of building energy estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 53-60.
    34. Eom, Jiyong & Clarke, Leon & Kim, Son H. & Kyle, Page & Patel, Pralit, 2012. "China's building energy demand: Long-term implications from a detailed assessment," Energy, Elsevier, vol. 46(1), pages 405-419.
    35. Kurt Kratena & Ina Meyer & Michael Wüger, 2009. "The Impact of Technological Change and Lifestyles on the Energy Demand of Households. A Combination of Aggregate and Individual Household Analysis," WIFO Working Papers 334, WIFO.
    36. Möller, Bernd & Wiechers, Eva & Persson, Urban & Grundahl, Lars & Connolly, David, 2018. "Heat Roadmap Europe: Identifying local heat demand and supply areas with a European thermal atlas," Energy, Elsevier, vol. 158(C), pages 281-292.
    37. Dmytro Romanchenko & Emil Nyholm & Mikael Odenberger & Filip Johnsson, 2019. "Flexibility Potential of Space Heating Demand Response in Buildings for District Heating Systems," Energies, MDPI, vol. 12(15), pages 1-23, July.
    38. Ouyang, Jinlong & Ge, Jian & Hokao, Kazunori, 2009. "Economic analysis of energy-saving renovation measures for urban existing residential buildings in China based on thermal simulation and site investigation," Energy Policy, Elsevier, vol. 37(1), pages 140-149, January.
    39. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    40. Seljom, Pernille & Lindberg, Karen Byskov & Tomasgard, Asgeir & Doorman, Gerard & Sartori, Igor, 2017. "The impact of Zero Energy Buildings on the Scandinavian energy system," Energy, Elsevier, vol. 118(C), pages 284-296.
    41. Pourazarm, Elham & Cooray, Arusha, 2013. "Estimating and forecasting residential electricity demand in Iran," Economic Modelling, Elsevier, vol. 35(C), pages 546-558.
    42. Hussain, Anwar & Rahman, Muhammad & Memon, Junaid Alam, 2016. "Forecasting electricity consumption in Pakistan: the way forward," Energy Policy, Elsevier, vol. 90(C), pages 73-80.
    43. Ó Broin, Eoin & Mata, Érika & Göransson, Anders & Johnsson, Filip, 2013. "The effect of improved efficiency on energy savings in EU-27 buildings," Energy, Elsevier, vol. 57(C), pages 134-148.
    44. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    45. Soren Leth-Petersen, 2002. "Micro Econometric Modelling of Household Energy Use: Testing for Dependence between Demand for Electricity and Natural Gas," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 57-84.
    46. Tooke, Thoreau Rory & van der Laan, Michael & Coops, Nicholas C., 2014. "Mapping demand for residential building thermal energy services using airborne LiDAR," Applied Energy, Elsevier, vol. 127(C), pages 125-134.
    47. Kontokosta, Constantine E. & Tull, Christopher, 2017. "A data-driven predictive model of city-scale energy use in buildings," Applied Energy, Elsevier, vol. 197(C), pages 303-317.
    48. Gooding, James & Crook, Rolf & Tomlin, Alison S., 2015. "Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighbouring buildings method," Applied Energy, Elsevier, vol. 148(C), pages 93-104.
    49. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    50. Levesque, Antoine & Pietzcker, Robert C. & Baumstark, Lavinia & De Stercke, Simon & Grübler, Arnulf & Luderer, Gunnar, 2018. "How much energy will buildings consume in 2100? A global perspective within a scenario framework," Energy, Elsevier, vol. 148(C), pages 514-527.
    51. van Sluisveld, Mariësse A.E. & Martínez, Sara Herreras & Daioglou, Vassilis & van Vuuren, Detlef P., 2016. "Exploring the implications of lifestyle change in 2°C mitigation scenarios using the IMAGE integrated assessment model," Technological Forecasting and Social Change, Elsevier, vol. 102(C), pages 309-319.
    52. Mastrucci, Alessio & Marvuglia, Antonino & Leopold, Ulrich & Benetto, Enrico, 2017. "Life Cycle Assessment of building stocks from urban to transnational scales: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 316-332.
    53. Magnus Moglia & Aneta Podkalicka & James McGregor, 2018. "An Agent-Based Model of Residential Energy Efficiency Adoption," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(3), pages 1-3.
    54. Tian, Wei & Liu, Yunliang & Heo, Yeonsook & Yan, Da & Li, Zhanyong & An, Jingjing & Yang, Song, 2016. "Relative importance of factors influencing building energy in urban environment," Energy, Elsevier, vol. 111(C), pages 237-250.
    55. Adom, Philip Kofi & Bekoe, William, 2012. "Conditional dynamic forecast of electrical energy consumption requirements in Ghana by 2020: A comparison of ARDL and PAM," Energy, Elsevier, vol. 44(1), pages 367-380.
    56. Mata, Érika & Kalagasidis, Angela Sasic & Johnsson, Filip, 2018. "Contributions of building retrofitting in five member states to EU targets for energy savings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 759-774.
    57. Nägeli, Claudio & Jakob, Martin & Catenazzi, Giacomo & Ostermeyer, York, 2020. "Policies to decarbonize the Swiss residential building stock: An agent-based building stock modeling assessment," Energy Policy, Elsevier, vol. 146(C).
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