Volume 20, Issue 3 (March 2020)
Modares Mechanical Engineering 2020, 20(3): 531-537 |
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Safikhani H, Loloee M. Multi-Objective Optimization of Comfort-Cost for Cooling and Heating Systems at the Faculty of Engineering of Arak University. Modares Mechanical Engineering 2020; 20 (3) :531-537
URL: http://mme.modares.ac.ir/article-15-32871-en.html
URL: http://mme.modares.ac.ir/article-15-32871-en.html
1- Mechanical Engineering Department, Engineering Faculty, Arak University, Arak, Iran , h-safikhani@araku.ac.ir
2- Mechanical Engineering Department, Engineering Faculty, Arak University, Arak, Iran
2- Mechanical Engineering Department, Engineering Faculty, Arak University, Arak, Iran
Abstract: (5412 Views)
In this paper, multi-objective optimization of the cooling and heating systems at the faculty of engineering of Arak University is investigated to increasing comfort and reducing the cost of energy. In the first step, the faculty building with 4 floors, 11800 square meters of infrastructure and 122 classrooms and rooms is modeled and the comfort and cost of the faculty are calculated. In the next step, a database of 2,000 faculties with different design variables was created and analyzed. Between the formed databases, buildings with the best objective functions are selected and presented in a Pareto front. Design variables are the 11 geometrical and non-geometrical factors affecting the comfort and cost of the faculty. The objective functions are the comfort, cost, and energy consumption. The results indicate that both absorption and compression systems have the ability to achieve acceptable levels of comfort, but the amount of energy consumed in the absorption chiller is higher than the energy consumption of the compression system, which indicates the necessity of using absorption systems in conditions of waste heat. Also, the results indicate that the absorption system, despite the higher energy consumption than the compression system, has lower energy consumption costs due to the difference between electricity and gas tariffs in Iran country and should be corrected.
Keywords: Comfort, Cost, Air Conditioning, Multi-Objective Optimization, Absorption and Compression Chillers, Energy Saving
Article Type: Original Research |
Subject:
Plumbing & Air Conditioning
Received: 2019/05/11 | Accepted: 2019/06/15 | Published: 2020/03/1
Received: 2019/05/11 | Accepted: 2019/06/15 | Published: 2020/03/1
References
1. Mahlatsi Malatji E, Zhang J, Xia X. A multiple objective optimisation model for building energy efficiency investment decision. Energy and Buildings. 2013;61:81-87. [Link] [DOI:10.1016/j.enbuild.2013.01.042]
2. Asadi E, Gameiro da Silva M, Henggeler Antunesc C, Diasc Y. Multi-objective optimization for building retrofit strategies: A model and an application. Energy and Buildings. 2012;44:81-87. [Link] [DOI:10.1016/j.enbuild.2011.10.016]
3. Ghaffari Jabari S, Ghafari Jabari S, Saleh E. Review Strategies for Improving the Design and Construction of Settlements in Tehran. Quarterly Journal of Energy Policy and Planning Research. 2013;1(1):115-132. [Persian] [Link]
4. Karmellos M, Kiprakis A, Mavrotas G. A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies. Applied Energy. 2015;139:131-150. [Link] [DOI:10.1016/j.apenergy.2014.11.023]
5. Magnier L, Haghighat F. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment. 2010;45(3):739-746. [Link] [DOI:10.1016/j.buildenv.2009.08.016]
6. Mofidi F, Akbari H. Integrated optimization of energy costs and occupants' productivity in commercial buildings. Energy and Buildings. 2016;129:247-260. [Link] [DOI:10.1016/j.enbuild.2016.07.059]
7. Penna P, Pradaa A, Cappellettib F, Gasparella A. Multi-objectives optimization of Energy Efficiency Measures in existing buildings Paola Pennaa. Energy and Buildings. 2015;95:57-69. [Link] [DOI:10.1016/j.enbuild.2014.11.003]
8. Delgarm N, Sajadi B, Kowsary F, Delgarm S. Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Applied Energy. 2016;170:293-303. [Link] [DOI:10.1016/j.apenergy.2016.02.141]
9. Gorji Mahalbani Y, Mofrad Boshehri E, Azizzadeh Arani R. The effect of window on the reduction of heating and cooling loads of buildings using simulation in design builder software. Renewable Energy. 2017;4(1):1-8. [Persian] [Link]
10. Yang R, Wang L. Multi-objective optimization for decision-making of energy and comfort management in building automation and control. Sustainable Cities and Society. 2012;2(1):1-7. [Link] [DOI:10.1016/j.scs.2011.09.001]
11. Hameed Shaikh P, Mohd.Nor N, Nallagownden P, Elamvazuthi I. Intelligent optimized control system for energy and comfort management in efficient and sustainable buildings. Procedia Technology. 2013;11:99-106. [Link] [DOI:10.1016/j.protcy.2013.12.167]
12. Bre F, Fachinotti VD. A computational multi-objective optimization method to improve energy efficiency and thermal comfort in dwellings. Energy and Buildings. 2017;154:283-294. [Link] [DOI:10.1016/j.enbuild.2017.08.002]
13. Edith Camporeale P, del Pilar Mercader Moyano M, Czajkowski JD. Multi-objective optimisation model: A housing block retrofit in Seville. Energy and Buildings. 2017;153:476-484. [Link] [DOI:10.1016/j.enbuild.2017.08.023]
14. Yu W, Li B, Jia H, Zhang M, Wang D. Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings. 2015;88:135-143. [Link] [DOI:10.1016/j.enbuild.2014.11.063]
15. Ascione F, Bianco N, De Stasio C, Maria Mauro G, Vanoli GP. Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort. Energy and Buildings. 2016;111:131-144. [Link] [DOI:10.1016/j.enbuild.2015.11.033]
16. Papantoniou S, Kolokotsa D, Kalaitzakis K. Optimization and control algorithms implemented in existing BEMS using a web based energy management and control system. Energy and Buildings. 2015;98:45-55. [Link] [DOI:10.1016/j.enbuild.2014.10.083]
17. Gou S, Nik MV, Scartezzini JL, Zhao Q, Li Zh. Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand. Energy and Buildings. 2018;169:484-506. [Link] [DOI:10.1016/j.enbuild.2017.09.095]
18. Gaonkar P, Bapat J, Das D. Location-aware multi-objective optimization for energy cost management in semi-public buildings using thermal discomfort information. Sustainable Cities and Society. 2018;40:174-181. [Link] [DOI:10.1016/j.scs.2017.12.021]
19. Torres-Rivas A, Palumbo M, Haddad A, Cabeza LF, Jiménez L, Boer D. Multi-objective optimisation of bio-based thermal insulation materials in building envelopes considering condensation risk. Applied Energy. 2018;224:602-614. [Link] [DOI:10.1016/j.apenergy.2018.04.079]
20. Khoroshiltseva M, Slanzi D, Poli I. A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices. Applied Energy. 2016;184:1400-1410. [Link] [DOI:10.1016/j.apenergy.2016.05.015]
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