The effect of using smart shadings on the thermal and visual performances of buildings in Iran: A numerical simulation

Document Type : Research Paper

Authors

School of Mechanical Engineering, College of Engineering, University of Tehran

Abstract

This paper presents controlling and optimizing the energy performance of buildings using smart shadings. Simulations are carried out using EnergyPlus and multi-objective optimization is performed by jEPlus+EA through NSGA-II algorithm. Optimization of control strategies is performed for a typical office room on the middle floor of a building in Tehran. Slat angle, solar radiation, and the material of smart windows are selected as decision variables. Also, the annual total building energy consumption, the predicted percentage of dissatisfaction (PPD), and the discomfort glare index (DGI) are considered as three objective functions minimized simultaneously. The weighted sum method to select the final answer of Pareto solutions is used. In the first strategy, a comparison of the results of optimization with the initial values when the angles of slats are constant and equal to 45° showed that the total annual energy consumption, DGI, and PPD indexes reduced up to 11.74%, 6.4%, and 46.6%, respectively. In the second strategy, the reductions were 28.73, 56.50, and 34.05%, respectively, in comparison with the double-glazing window. The results clearly show how the correct selection of architectural parameters and control strategies can greatly prevent energy losses while providing the thermal and visual comfort of the building occupants.

Keywords


[1] 2019 BP Energy Outlook, British Petroleum, London: UK, 2019.
[2] Amini, L. Saber Fattahi, P. Selinmanpour, N. Gol Ghahremani, N. Kaveh, F. Farmad, M. Tavanpour, G. Karaminia, M. Khodi, Energy Balance Sheet 1394, Ministry of Energy, Tehran: Iran, 2017.
[3] Dincer, On thermal energy storage systems and applications in buildings, Energy and Buildings, 2002, 34 (4), pp. 377-388.
[4] Pérez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy and Buildings, 2008, 40 (3), pp. 394-398.
[5] C.J. Skarning, C.A. Hviid, S. Svendsen, The effect of dynamic solar shading on energy, daylighting and thermal comfort in a nearly zero-energy loft room in Rome and Copenhagen, Energy and Buildings, 2017, 135, pp. 302-311.
[6] Tabadkani, M. Valinejad Shoubi, F. Soflaei, S. Banihashemi, Integrated parametric design of adaptive facades for user’s visual comfort, Automation in Construction, 2019, 106, pp. 102857.
[7] Wang, and S. Greenberg, Window operation and impacts on building energy consumption, Energy and Buildings, 2015, 92, pp. 313-321.
[8] Firlag, M. Yazdanian, C. Curcija, C. Kohler, S. Vidanovic, R. Hart, S. Czarnecki, Control algorithms for dynamic windows for residential buildings, Energy and Buildings, 2015, 109, pp. 157-173.
[9] Yeon, B. Yu, B. Seo, Y. Yoon, K.H. Lee, ANN based automatic slat angle control of venetian blind for minimized total load in an office building, Solar Energy, 2019, 180, pp. 133-145.
[10] Kirimtat, B.K. Koyunbaba, I. Chatzikonstantinou, S. Sariyildiz, Review of simulation modeling for shading devices in buildings, Renewable and Sustainable Energy Reviews, 2016, 53, pp. 23-49.
[11] Yun, D.Y. Park, K.S. Kim, Appropriate activation threshold of the external blind for visual comfort and lighting energy saving in different climate conditions, Building and Environment, 2017, 113, pp. 247-266.
[12] Hoffmann, E.S. Lee, A. McNeil, L. Fernandes, D. Vidanovic, A. Thanachareonkit, Balancing daylight, glare, and energy-efficiency goals: An evaluation of exterior coplanar shading systems using complex fenestration modeling tools, Energy and Buildings, 2016, 112, 2016, pp. 279-298.
[13] Xiong, A. Tzempelikos, Model-based shading and lighting controls considering visual comfort and energy use, Solar Energy, 2016, 134, pp. 416-428.
[14] Bellia, C. Marino, F. Minichiello, A. Pedace, An overview on solar shading systems for buildings, Energy Procedia, 2014, 62, pp. 309-317.
[15] Konstantoglou, A. Tsangrassoulis, Dynamic operation of daylighting and shading systems: A literature review, Renewable and Sustainable Energy Reviews, 2016, 60, pp. 268-283.
[16] Oleskowicz-Popiel, M. Sobczak, Effect of the roller blinds on heat losses through a double-glazing window during heating season in Central Europe, Energy and Buildings, 2014, 73, pp. 48-58.
[17] Tzempelikos, A.K. Athienitis, The impact of shading design and control on building cooling and lighting demand, Solar Energy, 2007, 81 (3), pp. 369-382.
[18] Tuhus-Dubrow, M. Krarti, Genetic-algorithm based approach to optimize building envelope design for residential buildings, Building and Environment, 2010, 45 (7), pp. 1574-1581.
[19] Shan, Optimization for heating, cooling and lighting load in building facade design, Energy Procedia, 2014, 57, pp. 1716-1725.
[20] 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, 2016, 170, pp. 293–303.
[21] Naderi, B. Sajadi, M. Akhavan, and E. Naderi, Multi-objective simulation-based optimization of controlled blind specifications to reduce energy consumption and thermal and visual discomfort: Case studies in Iran, Building and Environment, 2020, 169, 106570.
[22] Krarti, “A comparative energy analysis of dynamic external shadings for office buildings,” ASME Journal of Engineering for Sustainable Buildings and Cities, 2022, 3(2), 021001.
[23] De Luca, A. Sepúlveda, T. Varjas, “Multi-performance optimization of static shading devices for glare, daylight, view and energy consideration,” Building and Environment, 2022; 217, 109110.
[24] Valitabar, A. GhaffarianHoseini, A. GhaffarianHoseini, S. Attia, “Advanced control strategy to maximize view and control discomforting glare: a complex adaptive façade,” Architectural Engineering and Design Management, 2022, 18(6), pp. 829-49.
[25] Zhang, I. Korolija, Preforming complex parametric simulations with jEPlus, Proceeding of 9th International Conference on Sustainable Energy Technologies, Shanghai: China, 2010.
[26] Zhang, Use jEPlus as an efficient building design optimization tool, CIBSE AHRAE Technical Symposium, London: UK, 2012.
[27] G. Ellis, P.A Torcellini, D.B. Crawley, “Simulation of Energy Management Systems in EnergyPlus, Proceeding of Building Simulation Conference, Beijing: China, 2007.
[28] EnergyPlus Input Output Reference, U.S. Department of Energy, Washington D.C., 2020.
[29] G. Rodriguez, J.A. Yamín Garretón, A.E. Pattini, An epidemiological approach to Daylight discomfort glare, Building and Environment, 2017, 113, pp. 39-48.
[30] G. Hopkinson, Glare from daylighting in buildings, Applied Ergonomics, 1972, 3 (4), pp. 206-215.
[31] Hamedani, E. Solgi, H. Skates, T. Hine, R. Fernanado, J. Lyons, K. Dupre, Visual discomfort and glare assessment in office environments: A review of light-induced physiological and perceptual responses, Building and Environment, 2019, 153, pp. 267-280.
[32] de Dear and G.S. Brager, Developing an adaptive model of thermal comfort and preference, ASHRAE Transactions, 1998, 104 (Part 1), pp. 145-167.
[33] Fountain and E. A. Arens, Air movement thermal comfort for occupant, ASHRAE Journal, 1993, 35 (8), pp. 26-29.
[34] ANSI/ASHRAE Standard 55: Thermal Environmental Conditions for Human Occupancy, ASHRAE, Atlanta: GA, 2020.
[35] O. Fanger, Thermal Comfort: Analysis and Applications in Environmental Engineering, Danish Techinical Press, Copenhagen: Denmark, 1970.
[36] Asadi, M.G. da Silva, C.H. Antunes, L. Dias, A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB, Building and Environment, 2012, 56, pp. 370-378.
[37] Zhai, Y. Wang, Y. Huang, X. Meng, A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance, Renewable Energy, 2019, 134, pp. 1190-1199.
[38] Yusoff, M.S. Ngadiman, A.M. Zain, Overview of NSGA-II for optimizing machining process parameters, Procedia Engineering, 2011, 15, pp. 3978-3983.
[39] National Building Regulations of Iran: Part 19, Ministry of Roads & Urban Development, Tehran: Iran,
[40] C. Peel, B.L. Finlayson, T.A. McMahon, Updated world map of the Köppen-Geiger climate classification, Hydrology and Earth System Sciences, 2007, 11, pp. 1633-1644.
[41] T. Nguyen, S. Reiter, P. Rigo, A review on simulation-based optimization methods applied to building performance analysis, Applied Energy, 2014, 113, pp. 1043-1058.
[42] Wanga, H. Rivard, R. Zmeureanu, An object-oriented framework for simulation-based green building design optimization with genetic algorithms, Advanced Engineering Informatics, 2005, 19 (1), pp. 5-23.
[43] Delgarm, B. Sajadi, S. Delgarm, Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony (ABC), Energy and Building, 2016, 131, pp. 42-53.
[44] Karmellos, A. Kiprakis, G. Mavrotas, A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case
studies, Appl. Energy. 139, 131-150, 2015.
[45] Ryu, S. Kim, H. Wan, Pareto front approximation with adaptive weighted sum method in multiobjective simulation optimization., Proceedings of the 2009 Winter Simulation Conference, Austin: TX, 2009.