آنالیز بهره‌وری انرژی با درنظرگرفتن عدم‌قطعیت در ساختمان‌ها

[1] A. Allouhi, Y. El Fouih, T. Kousksou, A. Jamil, Y. Zeraouli, and Y. Mourad, “Energy consumption and efficiency in buildings: current status and future trends,” Journal of Cleaner production,vol. 109, pp. 118-130, 2015.

[2] C. J. Kibert, Sustainable construction: green building design and delivery, John Wiley & Sons, 2016.

[3] A. J. Marszal et al., “Zero Energy Building–A review of definitions and calculation methodologies,” Energy and buildings, vol. 43, no. 4, 2011.

[4] S. Pless and P. Paul Torcellini PhD, “Getting to net zero,” ASHRAE Journal, vol. 51, no. 9, 2009.

[5] D. B. Crawley, J. W. Hand, M. Kummert, and B. T. Griffith, “Contrasting the capabilities of building energy performance simulation programs,” Building and environment, vol. 43, no. 4, 2008.

[6] A.-T. Nguyen, S. Reiter, and P. Rigo, “A review on simulation-based optimization methods applied to building performance analysis,” Applied Energy, vol. 113, 2014.

[7] N. Delgarm, B. Sajadi, S. Delgarm, and F. Kowsary, “A novel approach for the simulation-based optimization of the buildings energy consumption using NSGA-II: Case study in Iran,” Energy and Buildings, vol. 127, 2016.

[8] N. Delgarm, B. Sajadi, F. Kowsary, and S. Delgarm, “Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO),” Applied Energy, vol. 170, 2016.

[9] L. Junghans and N. Darde, “Hybrid single objective genetic algorithm coupled with the simulated annealing optimization method for building optimization,” Energy and Buildings,vol. 86, 2015.

[10] S. N. Murray, B. P. Walsh, D. Kelliher, and D. O'Sullivan, “Multi-variable optimization of thermal energy efficiency retrofitting of buildings using static modelling and genetic algorithms–A case study,” Building and Environment,vol. 75, 2014.

[11] J. Wright and A. Alajmi, “Efficient Genetic Algorithm sets for optimizing constrained building design problem,” International Journal of Sustainable Built Environment, vol. 5, no. 1, 2016.

[12] W. Yu, B. Li, H. Jia, M. Zhang, and D. Wang, “Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design,” Energy and Buildings, vol. 88, pp. 135-143, 2015.

[13]  عباس محمدویسی، علیرضا حاتمی، «ارائه چارچوبی برای مدیریت انرژی خانه هوشمند: برنامه‌ریزی بهینه تجهیزات خانگی و برنامه‌ریزی بهینه بهره‌برداری از منابع انرژی تجدیدپذیر»، مجله مهندسی برق دانشگاه تبریز، جلد 45، شماره 1، بهار 1394.

[14]  مجید محمدپور، حمید پروین، «الگوریتم ژنتیک آشوبگونه مبتنی بر حافظه و خوشه‌بندی برای حل مسائل بهینه‌سازی پویا»، مجله مهندسی برق دانشگاه تبریز، جلد46، شماره 3، پاییز 1395.

[15] D. Celarec and M. Dolšek, “The impact of modelling uncertainties on the seismic performance assessment of reinforced concrete frame buildings,” Engineering Structures,vol. 52, pp. 340-354, 2013.

[16] A. B. Liel, C. B. Haselton, G. G. Deierlein, and J. W. Baker, “Incorporating modeling uncertainties in the assessment of seismic collapse risk of buildings,” Structural Safety, vol. 31, no. 2, 2009.

[17] M. Manfren, N. Aste, and R. Moshksar, "Calibration and uncertainty analysis for computer models–a meta-model based approach for integrated building energy simulation,” Applied energy, vol. 103, pp. 627-641, 2013.

[18] S. S. Amiri, M. Mottahedi, and S. Asadi, “Using multiple regression analysis to develop energy consumption indicators for commercial buildings in the US,” Energy and Buildings,vol. 109, pp. 209-216, 2015.

[19] M. J. Bordbari, A. R. Seifi, and M. Rastegar, “Probabilistic energy consumption analysis in buildings using point estimate method,” Energy, vol. 142, pp. 716-722, 2018.

[20] A. Ioannou and L. Itard, “Energy performance and comfort in residential buildings: Sensitivity for building parameters and occupancy,” Energy and Buildings, vol. 92, pp. 216-233, 2015.

[21] S. Sun, K. Kensek, D. Noble, and M. Schiler, “A method of probabilistic risk assessment for energy performance and cost using building energy simulation,” Energy and Buildings, vol. 110, pp. 1-12, 2016.

[22] L. Van Gelder, H. Janssen, and S. Roels, “Probabilistic design and analysis of building performances: methodology and application example,” Energy and Buildings, vol. 79, pp. 202-211, 2014.

[23] Z. Yang and B. Becerik-Gerber, “A model calibration framework for simultaneous multi-level building energy simulation,” Applied Energy, vol. 149, pp. 415-431, 2015.

[24] A. H. Neto and F. A. S. Fiorelli, “Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption,” Energy and buildings, vol. 40, no. 12, 2008.

[25] M. Krarti, Energy audit of building systems: an engineering approach, CRC press, 2016.

[26] M. Taleghani, M. Tenpierik, S. Kurvers, and A. Van Den Dobbelsteen, “A review into thermal comfort in buildings,” Renewable and Sustainable Energy Reviews,vol. 26, pp. 201-215, 2013.

[27] P. O. Fanger and J. Toftum, “Extension of the PMV model to non-air-conditioned buildings in warm climates,” Energy and buildings,vol. 34, no. 6, 2002.

[28] P. Fanger, “moderate thermal environments determination of the PMV and PPD indices and specification of the conditions for thermal comfort,” ISO 7730, 1984.

[29] K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II,” in International Conference on Parallel Problem Solving From Nature, Springer, pp. 849-858, 2000.

[30] Department of Energy (DOE). Commercial Reference Buildings. december 2017, https://energy.gov/eere/buildings/commercial-reference-buildings.

[31] ASHRAE Standard, “Standard 55-2013: Thermal environmental conditions for human occupancy,” American Society of Heating, Refrigerating, and air-conditioning Engineers, Atlanta, 2013.

[32] F. R. Cecconi, M. Manfren, L. C. Tagliabue, A. L. C. Ciribini, and E. De Angelis, “Probabilistic behavioral modeling in building performance simulation: A Monte Carlo approach,” Energy and Buildings,vol. 148, pp. 128-141, 2017.

[33] Q. Cheng, S. Wang, C. Yan, and F. Xiao, “Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings,” Applied Energy, vol. 185, pp. 1613-1624, 2017.

[34] L. C. Tagliabue, M. Manfren, A. L. C. Ciribini, and E. De Angelis, “Probabilistic behavioural modeling in building performance simulation—The Brescia eLUX lab,” Energy and Buildings,vol. 128, pp. 119-131, 2016.