بهینه سازی مصرف انرژی در بخش ساختمان با استفاده از شبکه عصبی و الگوریتم PSO (مطالعه موردی شهرستان بندرعباس)
الموضوعات :
فخری اله یاری
1
,
آزیتا بهبهانی نیا
2
,
حسین رحامی
3
,
مریم فراهانی
4
,
سمیرا خدیوی
5
1 - دانشجوی دکتری گروه محیط زیست، دانشکده کشاورزی و علوم پایه دانشگاه آزاد اسلامی واحد رودهن، رودهن، ایران.
2 - استادیار گروه محیط زیست، دانشکده کشاورزی و علوم پایه دانشگاه آزاد اسلامی واحد رودهن، رودهن، ایران. *(مسوول مکاتبات)
3 - دانشیار دانشکده علوم مهندسی پردیس دانشکده های فنی دانشگاه تهران، تهران، ایران.
4 - استادیار گروه محیط زیست، دانشکده کشاورزی و علوم پایه، دانشگاه آزاد اسلامی واحد رودهن، رودهن، ایران.
5 - استادیار گروه محیط زیست، دانشکده کشاورزی و علوم پایه، دانشگاه آزاد اسلامی واحد رودهن، رودهن، ایران.
تاريخ الإرسال : 03 الأربعاء , ذو القعدة, 1441
تاريخ التأكيد : 10 الثلاثاء , ربيع الأول, 1442
تاريخ الإصدار : 18 الأربعاء , جمادى الأولى, 1443
الکلمات المفتاحية:
نرم افزار Design Builder,
بهینه سازی,
انرژی,
ساختمان,
الگوریتم PSO,
شبکه عصبی,
ملخص المقالة :
زمینه و هدف: مصرف انرژی در ساختمان ها یک سوم مصرف انرژی سالانه کشور را تشکیل می دهد، بنابراین ارائه راهکارهایی که بتواند مصرف انرژی را در این بخش کاهش دهد، حائز اهمیت است.روش بررسی: با استفاده از پرسشنامه و نظرات کارشناسان، پارامترهای موثر در بهینه سازی انرژی در سازمان نظام مهندسی ساختمان بندرعباس شناسایی شد. متغیرهایی مانند جنس مواد دیوار و سقف، مساحت و نوع پنجره ها، ضخامت عایق دیوار و سقف انتخاب شدند. حالت های مختلف با نرم افزار Design Builder بررسی شد. با آموزش دو شبکه عصبی مجزا نحوه اتصال ورودی ها به دو خروجی مهم یعنی میزان انرژی و دی اکسید کربن بدست آمد. و بهینه سازی با استفاده از الگوریتم PSO انجام شد.یافته ها: در مدل به دست آمده دیوار آجری با ضخامت عایق 5 سانتی متر، سقف تیرچه با ضخامت عایق 5 سانتی متر، شیشه سه جداره، نسبت پنجره های شمالی و شرقی به دیوار در یک جهت 70 درصد، نسبت پنجره جنوبی به دیوار جنوبی بین 41 به 43 است. درصد و نسبت پنجره غربی به دیوار غربی بین 65 تا 67 درصد است که در آن میزان انرژی و دی اکسید کربن حداقل است.بحث و نتیجه گیری: اگر انرژی به عنوان تابع هدف انتخاب شود، نتایج به‌دست‌آمده از PSO کاملاً با نتایج بهینه‌سازی برای زمانی که تابع هدف مقدار دی اکسید کربن است، مطابقت دارد. این دو تابع با یکدیگر همسو هستند و بهینه سازی یکی منجر به بهینه سازی دیگری می شود.
المصادر:
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.
_||_
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.
