Optimizing energy consumption in the building sector using neural networks and PSO algorithms (Case study: Bandar Abbas city)
Subject Areas :
Air Pollution
fakhri allahyari
1
,
Azita Behbahaninia
2
,
Hossein Rahami
3
,
Maryam Farahani
4
,
Samira Khadivi
5
1 - PhD Student, Department of Environment, Faculty of Basic science and Agriculture, Islamic Azad University, Roudehen branch, Roudehen, Iran.
2 - Assistant Professor, Department of Environment, Faculty of Basic science and Agriculture, Islamic Azad University, Roudehen branch, Roudehen, Iran. *(Corresponding Author)
3 - Associate Professor, School of Engineering Science, Faculty of Engineering, University of Tehran, Iran.
4 - Assistant Professor, Department of Environment, Faculty of Basic science and Agriculture, Islamic Azad University, Roudehen branch, Roudehen, Iran.
5 - Assistant Professor, Department of Environment, Faculty of Basic science and Agriculture, Islamic Azad University, Roudehen branch, Roudehen, Iran.
Received: 2020-06-24
Accepted : 2020-10-27
Published : 2021-12-22
Keywords:
Optimization,
Design builder software,
PSO algorithm,
Building,
Energy,
Neural Network,
Abstract :
Background and Objective: Energy consumption in buildings accounts for one third of the country's annual energy consumption, so it is important to provide solutions that can reduce energy consumption in this sector.Material and Methodology: Using questionnaires and experts’ opinions, effective parameters in energy optimization in Construction Engineering Organization of Bandar Abbas were identified. Variables such as wall and ceiling material, area and type of windows, wall and ceiling insulation thickness were selected. Different modes were investigated with Design Builder software. By training two separate neural networks, how the inputs are connected to two important outputs, which is the amount of energy and carbon dioxide, was obtained. And optimization was performed using the PSO algorithm.Findings: In the obtained model, brick wall with insulation thickness of 5cm, beam roof with insulation thickness of 5cm, triple glazing, ratio of north and east windows to wall in the same direction 70%, ratio of south window to south wall between 41 to 43 percent and the ratio of the west window to the west wall is between 65 to 67 percent, in which the amount of energy and carbon dioxide is the minimum.Discussion and Conclusion: If the energy is selected as target function, the results obtained from the PSO are closely consistent with the optimization results for when the target function is the amount of carbon dioxide. These two functions are in line with each other, and optimizing one will lead to optimizing the other.
References:
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Ghafari Jabbari Sh, Saleh E (2013). Housing Design Solutions in Tehran Energy Optimization. Journal of Energy Planning and Policy Research, 1st year, No.1, 2013.
Hashemi F, Heidari SH (2012). Optimizing energy consumption in residential buildings in cold climates (Case Study: Ardabil City), Sofeh Magazine, No. 56. (In Persian)
Khorramabadi M, Shahi F (2014). The Role of Nineteen National Building Regulations (Energy Saving) on Modifying the Energy Consumption Model. First National Conference on Intelligent Building Management Systems with Energy Conservation Optimization Approach, Qazvin, Building Engineering System of Qazvin Province, 2014. (In Persian).
Naseri A, Mehregani A (2017). Investigation of the effect of physical properties of residential buildings on energy consumption (A Case Study of Khorramabad City). Iranian Journal of Architecture and Urban Development, No.14, pp. 59-73. (In Persian)
Khoda Karami J, Qobadi Parisa (2016). Optimize energy consumption in an office building equipped with intelligent management system. Journal of Energy Engineering and Management, No. 2, 2016. (In Persian)
Elsheikh, A.H., Elazig, M.abd., (2019). Review on applications of particle swarm optimization in solar energy systems., international journal of environmental science and technology,volume 16,issue 2, pp1159-1170.
Alzoubi Isham, Delavar Mahmoud R, Mirzaei Farhad, Nadjar Arrabi Babak (2018). Prediction of environmental indicators in land leveling using artificial intelligence techniques, Journal of Environmental Health Science and Engineering, Volume 16, pp 65–80 (2018 ).
Batra. u & Signal.s., (2017). Optimum level of insulation for energy efficient envelope of office buildings, international journal of environmental science and technology, (2017), volume 14, issue 11, pp2389-2398.
Carreras, J., Pozo, C., Boer, D., Guillen-Gosalbez, G., Caballero, J.A., RuizFemenia, R. & Jimenez, L. (2016). Systematic approach for the life cycle multi-objective optimization of buildings combining objective reduction and surrogate modeling. Energy and Buildings, 130, 506-518.
Casini .M, Smart Buildings: Advanced Materials and Nanotechnology to Improve Energy-Efficiency and Environmental Performance, Woodhead Publishing, 1st edition, pp. 109-125, 2016.
Delgarm, N., Sajadi, B., Kowsary, F. & Delgarm, S. (2016). Mluti-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Applied Energy, 170, 293-303.
Barkhudaryan Naira, Orosa José A, Roshan Gholamreza. (2013). A new procedure to analyze the effect of air changes in building energy consumption, Journal of Environmental Health Science and Engineering , 2014.
Kazanasmaz T, Uygun IE, Akkurt GG (2014). On the relation between architectural considerations and heating energy performance of Turkish residential buildings in Izmir, Energy and Buildings, Vol. 72, pp. 38-50.
Meteorological Organization of the country (2015), Hormozgan Meteorological Office, Hormozgan Meteorological Research Center, Learn to pronounce, Meteorological Yearbook of Hormozgan Province, 2014-2015 crop year, 2015. (In Persian)
Mechanic A, Shafiee M (2013). Building design optimization using a combination of genetic algorithm and neural network. The 7th Student Conference on Mechanical Engineering, 2013. (In Persian)
Sarabi M, Ebrahimpour A (2013). Introduction and application of energy saving optimization software in buildings. Third International Conference on New Approaches to Energy Conservation 2013. (In Persian)
Kalami Heris SM (2013). The theory of multilayer perceptron neural networks, or MLP, Artificial neural network superconductors, a Tutorial Film. (In Persian)
Kalami Heris SM (2013). The theory of fundamentals of Particle Swarm Optimization Algorithm, or PSO, Artificial neural network superconductors, a Tutorial Film. (In Persian)
Emamqolozadeh M, Salari M (2017). Optimization Energy Consumption in an office building by calculating the impact of external components and engine smarts. Journal of Geography Studies of Civil and Urban Management, Volume 3, No.14, 2017. (In Persian)
Rafieian M, Fath Jalali A, Dadashpour H (2011). Investigation and feasibility of the effect of form and density of residential blocks on energy consumption of the city, Case study of Hashtgerd new city. Armanshahr Journal, No. 6, pp. 107-116. (In Persian)
Mirzargar M, Raeisi A (2015). Improving energy consumption in the buiding industry by optimizing lighting systems. Third National Conference on Building Climate and Energy Conservation Optimization with Sustainable Development Approach, Esfahan Province Engineering Organization, 2015.
Hui S., Hongwei T., Athanasios T., The effect of reflective coatings on building surface temperatures, indoor environment and energy consumption—An experimental study , China, Energy and Buildings 43, pp. 573–580, 2011.