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

Yıl 2018, Cilt: 8 Sayı: 1, 35 - 49, 30.06.2018

Damien Gatt Charles Yousıf Maurizio Cellura Liberato Camıllerı

https://doi.org/10.36222/ejt.467910

Cited By: 1

Öz

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.

Anahtar Kelimeler

Stock modelling , EPBD cost-optimal method , Bayesian calibration , reference zones

Kaynakça

• [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
• [70] C. F. Reinhart, C. Cerezo Davila, Urban building energy modeling - A review of a nascent field, Building and Environment, 97 (2016), pp. 196–202
• [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
• [72] The R Project for Statistical Computing. The R Foundation. https://www.r-project.org/.
• [73] JAGS. http://mcmc-jags.sourceforge.net/
• [74] Stan. http://mc-stan.org/