Abstract
Building optimization measures are implemented to reduce energy consumption and environmental pollution. If energy reduction and optimization in the buildings are not measured, the national economy will be severely damaged. The energy consumption in buildings can be reduced by up to 50% by performing optimization measures in the building sector and applying Article 19 of National Building Regulations. In this study, the effective parameters on energy optimization were identified using questionnaires and expert opinions and then, the energy consumption and carbon dioxide were calculated by entering the parameters into DesignBuilder software. The parameters included types of wall and ceiling, area of windows, type of windows, and insulation of wall and ceiling, each of which contain different modes. In order to limit the problem space, a range of parameters changes in a specified interval was selected. Since it is impossible to model all probable modes, first a finite number of models was tested using the software and then, the interaction of inputs with two important outputs (energy and carbon dioxide) was obtained by training two separate neural networks. The network training facilitates the calculation of the amount of energy and carbon dioxide needed for any desired input needless of DesignBuilder software.
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References
Antipova E, Boer D, Guillen-Gosalbez G, Cabeza LF, Jimenez L (2014) Multi-objective optimization coupled with life cycle assessment for retrofitting building. Energy Build 82:92–99
Asadi E, Da Silva MG, Antunes CH, Dias L, Glicksman L (2014) Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application. Energy Build 81:356–444
Batra U, Signal S (2017) Optimum level of insulation for energy efficient envelope of office buildings. Int J Environ Sci Technol 14(11):2389–2398
Carreras J, Pozo C, Boer D, Guillen-Gosalbez G, Caballero JA, Ruiz Femenia R, Jimenez L (2016) Systematic approach for the life cycle multi-objective optimization of buildings combining objective reduction and surrogate modeling. Energy Build 130:506–518
Choudhary S (2015) Analysis of energy conservation of an institutional building using DesignBuilder software. IJMECH 4(1):133
Delgarm N, Sajadi B, Kowsary F, Delgarm S (2016) Multi-objective optimization of the building energy performance: a simulation-based approach by means of particle swarm optimization (PSO). Appl Energy 170:293–303
Ebrahimpour A, Karimi Vahed Y (2016) Appropriate methods of optimizing energy consumption in a Tabriz university building. Modares Mech Eng Res J 12(4):2012 (in Persian)
Elsheikh AH, Abd Elaziz M (2019) Review on applications of particle swarm optimization in solar energy systems. Int J Environ Sci Technol 16(2):1159–1170
Hashemi F, Heidari SH (2012) Optimizing energy consumption in residential buildings in cold climates (case study: Ardabil City), Sofeh Magazine, No. 56 (in Persian)
Iran National Building Regulations (2010) Energy efficiency. Bureau for compiling and promoting national regulations for buildings. Ministry of Housing and Urbanism IRI, Delhi
Kalami Heris SM (2013) The theory of multilayer perceptron neural networks, or MLP, artificial neural network superconductors, a tutorial film (in Persian)
Khoda Karami J, Parisa Q (2016) Optimize energy consumption in an office building equipped with intelligent management system. J Energy Eng Manag 2:2016 (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)
Kumar K, Parida M, Katiyar VK (2014) Optimized height of noise barrier for nonurban highway using artificial neural network. Int J Environ Sci Technol 11(3):719–730
Marino C (2015) Existing buildings and HVAC Systems: incidence of innovative surface finishes on the energy requirements. Energy Procedia 82:499–505
Mazo J, Delgado M, Marin JM, Zalba B (2012) Modeling a radiant floor system with Phase Change Material (PCM) integrated into a building simulation tool: Analysis of a case study of a floor heating system coupled to a heat pump. Energy Build 47:458–466
Mechanic A, Shafiee M (2013) Building design optimization using a combination of genetic algorithm and neural network. In: The 7th student conference on mechanical engineering, 2013 (in Persian)
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)
Naseri A, Mehregani A (2017) Investigation of the effect of physical properties of residential buildings on energy consumption (a case study of Khorramabad City). Iran J Archit Urban Dev 14:59–73 (in Persian)
Nasr Malek M, Vasiq B, Rahaei O (2016) The effect of building walls on energy consumption optimization. In: National conference on contemporary challenges in architecture, landscape and urban development, 2016 (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 J 6:107–116 (In Persian)
Saba (2017) Energy balance sheet of 2015, Deputy of Electricity and Energy Macro Electricity Planning Bureau (in Persian)
Sarabi M, Ebrahimpour A (2013) Introduction and application of energy saving optimization software in buildings. In: Third international conference on new approaches to energy conservation, 2013 (in Persian)
Susorova I, Angulo M, Bahrami P, Stephens B (2013) A model of vegetated exterior facades for evaluation of wall thermal performance. Build Environ 67:1–13
Tabares Velasco PC, Srebric J (2012) A heat transfer model for assessment of plant based roofing systems in summer conditions. Build Environ 49:310–323
Taqavi M (2014) Building energy optimization methods. first national conference on intelligent building management systems with energy conservation optimization approach, Qazvin, building engineering system of Qazvin Province, 2014 (in Persian)
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Allahyari, F., Behbahaninia, A., Rahami, H. et al. Development of a model for energy management in office buildings by neural networks (case study: Bandar Abbas). Int. J. Environ. Sci. Technol. 17, 3279–3288 (2020). https://doi.org/10.1007/s13762-019-02613-y
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DOI: https://doi.org/10.1007/s13762-019-02613-y