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AN INNOVATIVE APPROACH TO MANAGE UNCERTAINTIES AND STOCK DIVERSITY IN THE EPBD COST-OPTIMAL METHODOLOGY

Year 2018, Volume: 8 Issue: 1, 35 - 49, 30.06.2018
https://doi.org/10.36222/ejt.467910

Abstract

The EU Energy Performance of Buildings
Directive (EPBD) 2010/31/EU is a step in the right direction to promote near
zero energy buildings (NZEB) in a step-wise manner, starting with minimum
energy performance and cost optimal thresholds for “reference buildings” (RBs)
for each category. Nevertheless, a standard method for defining RBs does not
exist, which led to a great divergence between MS in the level of detail used
to define RBs for the EPBD cost-optimal analysis. Such lack of harmonisation
between MS is further evident given the resulting large discrepancies in energy
performance indicators even between countries having similar climate.
Furthermore, discrepancies of 30% or higher between measured energy performance
and that derived from the EPBD software induces uncertainty in the actual
operational savings of measures leading to cost-optimality or NZEB in the
simulated environment. This research proposes a robust and innovative framework
to better handle uncertainties in the EPBD cost-optimal method both in the
building software input parameters and in the global Life Cycle Costings (LCC),
making the EPBD more useful for policy makers and ensuring a more harmonised
approach among MS. The concept behind the proposed framework is the combination
of a stochastic EPBD cost-optimal approach with Bayesian bottom-up calibrated
stock-modelling. A new concept of “reference zoning” versus the “reference
buildings” approach is also introduced in this research, which aims at
providing a simpler and more flexible aggregation of energy performance for the
more complex commercial building stock.

References

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  • [10] C. Tweed, Supporting argumentation practices in urban planning and design, Computer Environment & Urban Systems, 22 (1998),4, pp. 351–363.
  • [11] H. Lim, Z. J. Zhai, Review on stochastic modeling methods for building stock energy prediction, Building Simulation, 10 (2017), 5, pp. 607–624
  • [12] S. P. Corgnati, E. Fabrizio, M. Filippi, V. Monetti, Reference buildings for cost optimal analysis: Method of definition and application, Applied Energy, 102 (2013), pp. 983–993
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  • [35] P. Ferrante, G. Peri, G. Rizzo, G. Scaccianoce, V. Vaccaro, Old or new occupants of energy rehabilitated buildings,Two different approaches for hierarchizing group of buildings, Sustainable Cities and Society, 34 (2017), pp. 385–393
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  • [37] I. Sartori, N. H. Sandberg, H. Brattebø, Dynamic building stock modelling: General algorithm and exemplification for Norway, Energy and Buildings, 132 (2016), pp. 13–25
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  • [39] N. Tornay, R. Schoetter, M. Bonhomme, S. Faraut, V. Masson, GENIUS: A methodology to define a detailed description of buildings for urban climate and building energy consumption simulations, Urban Climate, 20 (2016), pp. 75–93
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  • [45] G. V. Fracastoro, M. Serraino, A methodology for assessing the energy performance of large scale building stocks and possible applications, Energy and Buildings, 43 (2011), pp.844–852
  • [46] R. Choudhary, Energy analysis of the non-domestic building stock of Greater London, Building and Environment, 51 (2012), pp. 243–254
  • [47] D. Gatt, C. Yousif, Renovating Primary school buildings in Malta to achieve cost-optimal energy performance and comfort levels, SBE 16 Malta International Conference, Malta, 2016, pp. 453–460
  • [48] T. Alves, L. Machado, R. G. de Souza, P. de Wilde, A methodology for estimating office building energy use baselines by means of land use legislation and reference buildings, Energy and Buildings, 143 (2017), pp. 100–113
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  • [59] N. Fumo, A review on the basics of building energy estimation, Renewable Sustainable Energy Review, 31 (2014), pp.53–60
  • [60] A. T. Booth, R. Choudhary, D. J. Spiegelhalter, Handling uncertainty in housing stock models, Building and Environment, 48 (2012), pp. 35–47
  • [61] E. Naber, R. Volk, F. Schultmann, From the Building Level Energy Performance Assessment to the National Level: How are Uncertainties Handled in Building Stock Models, Procedia Engineering, 180 (2017), pp. 1443–1452
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  • [64] A. Mastrucci, P. Pérez-López, E. Benetto, U. Leopold, I. Blanc, Global sensitivity analysis as a support for the generation of simplified building stock energy models, Energy and Buildings, 149 (2017), pp. 368–383.
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  • [71] T. Dogan, C. Reinhart, P. Michalatos, Autozoner: an algorithm for automatic thermal zoning of buildings with unknown interior space definitions, Journal of Building Performance Simulation, 9 (2016), 2, pp. 176–189
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Year 2018, Volume: 8 Issue: 1, 35 - 49, 30.06.2018
https://doi.org/10.36222/ejt.467910

Abstract

References

  • [1] Directive 2010/31/EU of the European Parliament and of the council of 19 May 2010 on the energy performance of buildings (recast), European parliament, 2010
  • [2] H. Khatib, IEA World Energy Outlook 2011-A comment, Energy Policy, 48 (2012), pp. 737–743
  • [3] Energy Roadmap 2050, European Union, 2012
  • [4] Y. Xing, N. Hewitt, P. Griffiths. Zero carbon buildings refurbishment––A Hierarchical pathway, Renewable ans Sustainable Energy Reviews, 15 (2011), 6, pp. 3229–3236
  • [5] T. Tsoutsos, S. Tournaki, C. A. de Santos, R. Vercellotti. Nearly Zero Energy Buildings Application in Mediterranean Hotels, Energy Procedia, 42 (2013), pp. 230–238
  • [6] P. Caputo, G. Pasetti, Overcoming the inertia of building energy retrofit at municipal level: The Italian challenge, Sustainable Cities and Society, 15 (2015), pp. 120–134
  • [7] D. Popescu, S. Bienert, C. Schützenhofer, R. Boazu, Impact of energy efficiency measures on the economic value of buildings, Applied Energy, 89 (2012), 1, pp. 454–463
  • [8] It Pays to Renovate, http://renovate-europe.eu/wp-content/uploads/2015/09/RE_IT_PAYS_TO_RENOVATE_brochure_v05_spreads.pdf.
  • [9] I. Ballarini, S. P. Corgnati, V. Corrado, Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project, Energy Policy, 68 (2014), pp. 273–284
  • [10] C. Tweed, Supporting argumentation practices in urban planning and design, Computer Environment & Urban Systems, 22 (1998),4, pp. 351–363.
  • [11] H. Lim, Z. J. Zhai, Review on stochastic modeling methods for building stock energy prediction, Building Simulation, 10 (2017), 5, pp. 607–624
  • [12] S. P. Corgnati, E. Fabrizio, M. Filippi, V. Monetti, Reference buildings for cost optimal analysis: Method of definition and application, Applied Energy, 102 (2013), pp. 983–993
  • [13] A. Schaefer, E. Ghisi, Method for Obtaining Reference Buildings, Energy and Buildings, 128 (2016), pp. 660–672
  • [14] T. Buso, S. P. Corgnati, A customized modelling approach for multi-functional buildings – Application to an Italian Reference Hotel, Applied Energy, 190 (2017), pp. 1302–1315
  • [15] L. Tronchin, K. Fabbri, A Round Robin Test for buildings energy performance in Italy, Energy and Buildings, 42 (2010), 10, pp. 1862–1877
  • [16] S. Petersen, C. A. Hviid, THE EEPD: Comparison of Calculated and Actual Energy Use in a Danish Office Building, Build. Simul. Optim. Conf., 2012, pp. 43–48
  • [17] E. Burman, D. Mumovic, J. Kimpian, Towards measurement and verification of energy performance under the framework of the European directive for energy performance of buildings, Energy, 77 (2014), pp. 153–163.
  • [18] D. D’Agostino, Assessment of the progress towards the establishment of definitions of Nearly Zero Energy Buildings (nZEBs) in European Member States, Journal of Building Engineering, 1(2015), pp. 20–32
  • [19] J. Kurnitski, T. Buso, S. P. Corgnati, A. Derjanecz, A. Litiu, nZEB definitions in Europe, REHVA European HVAC Journal, 51 (2014), 2, pp 6–9
  • [20] A. T. Booth, R. Choudhary, Decision making under uncertainty in the retrofit analysis of the UK housing stock: Implications for the Green Deal, Energy and Buildings, 64 (2013), pp. 292–308
  • [21] J. Sokol, C. Cerezo Davila, C. F. Reinhart, Validation of a Bayesian-based method for defining residential archetypes in urban building energy models, Energy and Buildings, 134 (2017), pp.11–24
  • [22] S. Attia, H. Mohamed, S. Carlucci, P. Lorenzo, S. Bucking, A. Hasan: Building performance optimization of net zero-energy buildings in: Modeling, design, and optimization of net zero-energy buildings (ED. A. Athienitis , W. O'Brien) , 2015, pp. 175–206.
  • [23] T. Loga et al., Use of building typologies for energy performance assessment of national building stocks existent experiences in European countries and common approach, TABULA project team, 2010
  • [24] EPISCOPE, Welcome to the joint EPISCOPE and TABULA, http://episcope.eu/index.php?id=97.
  • [25] E. G. Dascalaki, K. G. Droutsa, C. A. Balaras, S. Kontoyiannidis, Building typologies as a tool for assessing the energy performance of residential buildings - A case study for the Hellenic building stock, Energy and Buildings, 43 (2011), 12, pp. 3400–3409
  • [26] P. Florio, O. Teissier, Estimation of the energy performance certificate of a housing stock characterised via qualitative variables through a typology-based approach model: A fuel poverty evaluation tool, Energy and Buildings, 89 (2015), pp. 39–48
  • [27] J. Kragh, K. B. Wittchen, Development of two Danish building typologies for residential buildings, Energy and Buildings, 68 (2014), pp. 79–86
  • [28] N. Diefenbach et al., Application of Building Typologies for Modelling the Energy Balance of the Residential Building Stock, Institut Wohnen und Umwelt, Darmstadt, 2012
  • [29] T. Loga, B. Stein, N. Diefenbach, TABULA building typologies in 20 European countries—Making energy-related features of residential building stocks comparable, Energy and Buildings 132 (2016), pp.4–12
  • [30] T. Csoknyai et al. Building stock characteristics and energy performance of residential buildings in Eastern-European countries, Energy and Buildings, 132 (2016), pp. 39–52
  • [31] C. A. Balaras, E. G. Dascalaki, K. G. Droutsa, S. Kontoyiannidis, Empirical assessment of calculated and actual heating energy use in Hellenic residential buildings, Applied Energy, 164 (2016), pp. 115–132
  • [32] D. K. Serghides, S. Dimitriou, M. C. Katafygiotou, Towards European targets by monitoring the energy profile of the Cyprus housing stock, Energy and Buildings, 132 (2016), pp. 130–140
  • [33] Magrini A., Magnani L., Pernetti R., Opaque building envelope, in: Building Refurbishment for Energy Performance (Ed. A. Magrini ), Green Energy and Technology, Springer, Cham, 2014, pp 1-59
  • [34] D. D’Agostino et al., Synthesis Report on the National Plans for Nearly Zero Energy Buildings ( NZEBs ) Progress of Member States towards NZEBs, European Union, 2016
  • [35] P. Ferrante, G. Peri, G. Rizzo, G. Scaccianoce, V. Vaccaro, Old or new occupants of energy rehabilitated buildings,Two different approaches for hierarchizing group of buildings, Sustainable Cities and Society, 34 (2017), pp. 385–393
  • [36] C. Cerezo Davila, C. F. Reinhart, J. L. Bemis, Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets, Energy, 117 (2016), pp. 237–250
  • [37] I. Sartori, N. H. Sandberg, H. Brattebø, Dynamic building stock modelling: General algorithm and exemplification for Norway, Energy and Buildings, 132 (2016), pp. 13–25
  • [38] I. Ballarini, V. Corrado, A New Methodology for Assessing the Energy Consumption of Building Stocks, Energies, 10 (2017), no. 8:1102
  • [39] N. Tornay, R. Schoetter, M. Bonhomme, S. Faraut, V. Masson, GENIUS: A methodology to define a detailed description of buildings for urban climate and building energy consumption simulations, Urban Climate, 20 (2016), pp. 75–93
  • [40] D. K. Serghides, S. Dimitriou, M. C. Katafygiotou, M. Michaelidou, Energy efficient refurbishment towards nearly zero energy houses, for the mediterranean region, Energy Procedia 83 (2015), pp. 533–543
  • [41] P. Torcellini, M. Deru, B. Griffith, K. Benne, DOE Commercial Building Benchmark Models Preprint, ACEEE Summer Study Energy Efficiency in Buildings, 12 (2008)
  • [42] A. Brandão de Vasconcelos, M. D. Pinheiro, A. Manso, A. Cabaço, A Portuguese approach to define reference buildings for cost-optimal methodologies, Applied Energy, 140 (2015), pp. 316–328
  • [43] S. Moffatt, Stock aggregation, Methods for Evaluating the Environmental Performance of Building Stocks, Annex 31 Energy-Related Environmental Impact of Buildings, Canada Mortgage and Housing Corporation, 2004
  • [44] G. Kazas, E. Fabrizio, M. Perino, Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study, Applied Energy, 193 (2017), pp. 243–262
  • [45] G. V. Fracastoro, M. Serraino, A methodology for assessing the energy performance of large scale building stocks and possible applications, Energy and Buildings, 43 (2011), pp.844–852
  • [46] R. Choudhary, Energy analysis of the non-domestic building stock of Greater London, Building and Environment, 51 (2012), pp. 243–254
  • [47] D. Gatt, C. Yousif, Renovating Primary school buildings in Malta to achieve cost-optimal energy performance and comfort levels, SBE 16 Malta International Conference, Malta, 2016, pp. 453–460
  • [48] T. Alves, L. Machado, R. G. de Souza, P. de Wilde, A methodology for estimating office building energy use baselines by means of land use legislation and reference buildings, Energy and Buildings, 143 (2017), pp. 100–113
  • [49] L. G. Swan, V. I. Ugursal, Modeling of end-use energy consumption in the residential sector: A review of modeling techniques, Renewable and Sustainable Energy Review, 13 (2009),8, pp. 1819–1835
  • [50] M. Kavgic, A. Mavrogianni, D. Mumovic, A. Summerfield, Z. Stevanovic, M. Djurovic-Petrovic. A review of bottom-up building stock models for energy consumption in the residential sector, Building and Environment, 45 (2010),7, pp. 1683–1697
  • [51] Nic Rivers, Mark Jaccard, Combining Top-Down and Bottom-Up Approaches To Energy-Economy Modeling Using Discrete Choice Methods, The Energy Journal, 26 (2005), pp. 83–106
  • [52] W. Tian, R. Choudhary, A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London, Energy and Buildings, 54 (2012), pp. 1–11
  • [53] EN ISO 13790: 2008 Energy performance of buildings-Calculation of energy use for space heating and cooling, International Organization for Standardization, 2008
  • [54] CIBSE AM 11: 2015 Building performance modelling Building performance modelling. CIBSE, London, 2015
  • [55] The United States Department of Energy, EnergyPlus, https://energyplus.net/.
  • [56] F. Kentli, M. Yilmaz Mathematical modelling of two-axis photovoltaic system with improved efficiency. Elektronika Ir Elektrotechnika, 21(4), (2015), pp. 40-43.
  • [57] Thermal energy system specialists, TRNSYS, http://www.trnsys.com/.
  • [58] A. Foucquier, S. Robert, F. Suard, L. Stéphan, A. Jay, State of the art in building modelling and energy performances prediction: A review, Renewable Sustainable Energy Review, 23 (2013), pp. 272–288
  • [59] N. Fumo, A review on the basics of building energy estimation, Renewable Sustainable Energy Review, 31 (2014), pp.53–60
  • [60] A. T. Booth, R. Choudhary, D. J. Spiegelhalter, Handling uncertainty in housing stock models, Building and Environment, 48 (2012), pp. 35–47
  • [61] E. Naber, R. Volk, F. Schultmann, From the Building Level Energy Performance Assessment to the National Level: How are Uncertainties Handled in Building Stock Models, Procedia Engineering, 180 (2017), pp. 1443–1452
  • [62] M. Riddle, R. T. Muehleisen, A Guide to Bayesian Calibration of Building Energy Models, ASHRAE/IBPSA-USA Building Simulation Conference, Atlanta, 2014
  • [63] W. Tian, Q. Wang, J. Song, S. Wei, Calibrating Dynamic Building Energy Models using Regression Model and Bayesian Analysis in Building Retrofit Projects, IBPSA eSim 2014, 2014
  • [64] A. Mastrucci, P. Pérez-López, E. Benetto, U. Leopold, I. Blanc, Global sensitivity analysis as a support for the generation of simplified building stock energy models, Energy and Buildings, 149 (2017), pp. 368–383.
  • [65] M. C. Kennedy, A. O’Hagan, Bayesian calibration of computer models, Journal of the Royal Statistical Society, Statistical Methodology Series B, 63 (2001), 3, pp. 425–464
  • [66] Y. Yamaguchi, R. Choudhary, A. Booth, Y. Suzuki, Y. Shimoda, Urban-scale energy modelling of food supermarket considering uncertainty, , Building Simulation (Ed. E.Wurtz), 13th International Conference of the International Building Performance Simulation Association France, 2013, Vol. 1, pp. 1326-1333
  • [67] F. Zhao, S. H. Lee, G. Augenbroe, Reconstructing building stock to replicate energy consumption data, Energy and Buildings, 117 (2016), pp. 301–312
  • [68] ANSI/ASHRAE, ASHRAE Guideline 14-2014 Measurement of Energy and Demand Savings, ASHRAE, 2014
  • [69] Y. Heo, Bayesian Calibration of Building Energy Models for Energy Retrofit Decision-Making under Uncertainty, Ph. D. thesis, Georgia Institute of Technology, USA, 2011
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Details

Primary Language English
Journal Section Research Article
Authors

Damien Gatt This is me

Charles Yousıf This is me

Maurizio Cellura This is me

Liberato Camıllerı This is me

Publication Date June 30, 2018
Published in Issue Year 2018 Volume: 8 Issue: 1

Cite

APA Gatt, D., Yousıf, C., Cellura, M., Camıllerı, L. (2018). AN INNOVATIVE APPROACH TO MANAGE UNCERTAINTIES AND STOCK DIVERSITY IN THE EPBD COST-OPTIMAL METHODOLOGY. European Journal of Technique (EJT), 8(1), 35-49. https://doi.org/10.36222/ejt.467910

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