EDP Sciences logo
Access to the submission and peer-review system
Search
Menu
All issues Volume 4 (2019) Sust. Build., 4 (2019) 5 References
Issue
Sust. Build.
Volume 4, 2019
Zero Energy Mass Custom Homes
Article Number 5
Number of page(s) 14
DOI https://doi.org/10.1051/sbuild/2019006
Published online 11 December 2019
  1. EU, Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings (recast), Off. J. Eur. Union 13–35 (2010) [Google Scholar]
  2. M. Ferrara, V. Monetti, E. Fabrizio et al., Cost-optimal analysis for nearly zero energy buildings design and optimization: a critical review, Energies 11, 1–32 (2018) [CrossRef] [Google Scholar]
  3. E. Pikas, M. Thalfeldt, J. Kurnitski, Cost optimal and nearly zero energy building solutions for office buildings, Energy Build. (2014) [Google Scholar]
  4. J. Kurnitski, A. Saari, T. Kalamees, M. Vuolle, J. Niemelä, T. Tark, Cost optimal and nearly zero (nZEB) energy performance calculations for residential buildings with REHVA definition for nZEB national implementation, Energy Build. 43 , 3279–3288 (2011) [CrossRef] [Google Scholar]
  5. D. D’Agostino, D. Parker, A framework for the cost-optimal design of nearly zero energy buildings (NZEBs) in representative climates across Europe, Energy (2018) [Google Scholar]
  6. BPIE, Nearly Zero Energy Buildings in Europe, 2016 [Google Scholar]
  7. S.P. Corgnati, E. Fabrizio, M. Filippi, V. Monetti, ‘Reference buildings for cost optimal analysis: Method of definition and application, Appl. Energy (2013) [Google Scholar]
  8. F. Garde et al., Design of net zero energy buildings: Feedback from international projects, Energy Proc. 61, 995–998 (2014) [CrossRef] [Google Scholar]
  9. BPIE, Cost Optimality: Discussing methodology and challenges within the recast Energy Performance of Buildings Directive, 2010 [Google Scholar]
  10. BSI ISO 15686-5, BS ISO 15686-5:2008-Buildings & constructed assets − Service life planning − Part 5: Life cycle costing, Int. Stand. (2008) [Google Scholar]
  11. A.T. Nguyen, S. Reiter, P. Rigo, A review on simulation-based optimization methods applied to building performance analysis, Appl. Energy (2014) [Google Scholar]
  12. S. Attia, M. Hamdy, W. O’Brien, S. Carlucci, Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design, Energy Build. 60 , 110–124 (2013) [CrossRef] [Google Scholar]
  13. V. Machairas, A. Tsangrassoulis, K. Axarli, Algorithms for optimization of building design: a review, Renew. Sustain. Energy Rev. (2014) [Google Scholar]
  14. US Department of Energy and others, A common definition for zero energy buildings, Change 5 , 22 (2014) [Google Scholar]
  15. E.M. Malatji, J. Zhang, X. Xia, A multiple objective optimisation model for building energy efficiency investment decision, Energy Build. (2013) [Google Scholar]
  16. M. Hamdy, G.M. Mauro, Multi-objective optimization of building energy design to reconcile collective and private perspectives: CO2-eq vs. Discounted payback time, Energies (2017) [Google Scholar]
  17. H. Iba, C.C. Aranha, Introduction to genetic algorithms, Adaptation, Learning, and Optimization (2012) [CrossRef] [Google Scholar]
  18. M. Fesanghary, S. Asadi, Z.W. Geem, Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm, Build. Environ. (2012) [Google Scholar]
  19. D. Tuhus-Dubrow, M. Krarti, Genetic-algorithm based approach to optimize building envelope design for residential buildings, Build. Environ. (2010) [Google Scholar]
  20. J.A. Wright, H.A. Loosemore, R. Farmani, Optimization of building thermal design and control by multi-criterion genetic algorithm, Energy Build. (2002) [Google Scholar]
  21. K.J. Lomas, H. Eppel, Sensitivity analysis techniques for building thermal simulation programs, Energy Build. (1992) [Google Scholar]
  22. J.C. Lam, S.C.M. Hui, Sensitivity analysis of energy performance of office buildings, Build. Environ. (1996) [Google Scholar]
  23. P. Heiselberg, H. Brohus, A. Hesselholt, H. Rasmussen, E. Seinre, S. Thomas, Application of sensitivity analysis in design of sustainable buildings, Renew. Energy (2009) [Google Scholar]
  24. M.D. Morris, Factorial sampling plans for preliminary computational experiments, Technometrics (1991) [Google Scholar]
  25. M. Cervantes, The Monte Carlo Method, Math. Sci. Eng. (1972) [Google Scholar]
  26. University of Washington, Machine Learning: Clustering & Retrieval. [Online]. Available: https://www.coursera.org/lecture/ml-clustering-and-retrieval/complexity-of-brute-force-search-5R6q3. [Accessed: 10-Apr- 2019] [Google Scholar]
  27. G. Chiandussi, M. Codegone, S. Ferrero, F.E. Varesio, Comparison of multi-objective optimization methodologies for engineering applications, Comput. Math. Appl. (2012) [Google Scholar]
  28. T. Hatt et al., Kostenoptimierte Gebäude im Lebenszyklus, in Economicum Session 7 (2018) [Google Scholar]
  29. Passive House Institute, Passive House Planning Package (PHPP), passivehouse.com (2015) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.