• الصفحة الرئيسية
  • کاهش مصرف انرژی در ساختمان بوسیله بهینه سازی جدار سقف ؛ نمونه موردی: ساختمان مسکونی سه طبقه در شهر شیراز

شارک

عنوان URL للمقالة


رقم المقالة : JEST-2103-5326 (R1) زيارة : 230 الصفحة: 205 - 220

10.30495/jest.2022.59492.5326

نوع المخطوط: ابحاث

کاهش مصرف انرژی در ساختمان بوسیله بهینه سازی جدار سقف ؛ نمونه موردی: ساختمان مسکونی سه طبقه در شهر شیراز

الموضوعات :

خسرو موحد 1 , پری ناز کشتکاران 2 , زهرا برزگر مروستی 3

1 - دکتری، گروه معماری، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
2 - دانشیار، گروه معماری، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران. *(مسوول مکاتبات)
3 - استادیار مدعو ، گروه معماری، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران

تاريخ الإرسال : 24 الإثنين , رجب, 1442 تاريخ التأكيد : 01 الأحد , جمادى الأولى, 1443 تاريخ الإصدار : 21 الأحد , شوال, 1443

الکلمات المفتاحية: بهینه سازی جداره, انرژی پلاس, سقف, الگوریتم ژنتیک, مصرف انرژی,

ملخص المقالة :

زمینه و هدف: جداره های ساختمان خارجی ترین پوسته بنا هستند که در معرض مستقیم هوا و تغییرات دما قرار دارند. لذا به لحاظ تبادل حرارت و کنترل مصرف انرژی ساختمان حایز اهمیت می باشند. جدار سقف ساختمان از جنبه تبادل حرارت، نسبت به سایر جداره های بنا از اهمیت بیشتری برخوردار است؛ چراکه به عنوان یک جداره وسیع افقی مساحت و زمان بیشتری را نسبت به سایرجدارها در معرض تابش خورشید و سایرعوامل جوی است؛ و تبادل حرارتی این جداره نسبت به سایر جداره های بنا بیشتر است. این مقاله با هدف کاهش انرژی مصرفی ساختمان در شهر شیراز به وسیله بهینه سازی جداره سقف ساختمان به دنبال پاسخ به این سوال است که: " بهینه ترین حالت آرایش مصالح و بهترین تکنیک غیر فعال سقف در راستای کاهش مصرف انرژی در اقلیم گرم و خشک شیرازکدام است و تا چه اندازه مصرف انرژی را کاهش خواهد داد؟ " روش بررسی: روش تحقیق کمی با استفاده از نرم افزار شبیه ساز انرژی "انرژی پلاس" می باشد. این تحقیق در سال 1398 انجام شده و داده های آب و هوایی شهر شیراز در این بازه زمانی یکساله از سایت انرژی پلاس استخراج شده است. جهت محاسبه انرژی مصرفی بنا، ساختمان انتخابی در نرم افزار انرژی پلاس مدل سازی و جهت پیشنهاد بهینه ترین حالت متغیرهای سقف از الگوریتم ژنتیک استفاده گردید؛ تابع هدف این الگوریتم کمینه سازی مصرف انرژی ساختمان بوده است. یافته ها: در این راستا متغیر های سقف ذیل سه دسته: سیستم غیر فعال انرژی، ویژگی های کالبدی سقف و موقعیت قرارگیری تعریف و روند بهینه سازی توسط الگوریتم ژنتیک و نرم افزار انرژی پلاس صورت گرفت. بحث و نتیجه گیری: نهایتا مدل های بهینه آرایش مصالح سقف ساختمان مسکونی سه طبقه در شهر شیراز ارائه شد. محاسبات شبیه ساز انرژی ساختمان و خروجی تابع هدف الگوریتم ژنتیک نشان داد که بهینه ترین آرایش مصالح سقف مصرف انرژی ساختمان را به میزان 6/9 درصد کاهش میدهد؛ و تکنیک های بهینه طراحی سیستم غیر فعال انرژی شامل: بام سبز، عایق حرارتی، سقف کاذب، به ترتیب 1/9 ، 4/13و 6/12 درصد مصرف انرژی ساختمان را کاهش داده اند.

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_||_

  1. Mohammadnejad, M. Ghazvini, T.M.I. Mahlia , A. Andriyana. A review on energy scenario and sustainable energy in Iran.Renewable and Sustainable Energy Reviews. (2011) .15 ,4652– 4658
    2.
  2. Ramin H, Hanafizadeh P, Akhavan-Behabadi MA. Determination of optimum insulation thickness in different wall orientations and locations in Iran. Adv Build Energy Res [Internet]. 2016;10(2):149–71. Available from: http://dx.doi.org/10.1080/17512549.2015.1079239
    3.
  3. Sharifi , Yamagata Y. Roof ponds as passive heating and cooling systems: A systematic review. Appl Energy. 2015;160:336–57.
    4.
  4. Goudarzi H, Mostafaeipour A. Energy saving evaluation of passive systems for residential buildings in hot and dry regions. Renew Sustain Energy Rev [Internet]. 2017;68:432–46. Available from: http://dx.doi.org/10.1016/j.rser.2016.10.002
    5.
  5. Shirzadi M, Nagashzadeghan M. Building Energy Optimization using Sequential Search Approach for Different Climates of Iran. Int J Renew Energy Res. 2015;5(1):210–6.
    6.
  6. Refahi AH, Talkhabi H. Investigating the effective factors on the reduction of energy consumption in residential buildings with green roofs. Renew Energy. 2015;80:595–603.
    7.
  7. Gong X, Akashi Y, Sumiyoshi D. Optimization of passive design measures for residential buildings in different Chinese areas. Build Environ [Internet]. 2012;58:46–57. Available from: http://dx.doi.org/10.1016/j.buildenv.2012.06.014
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  8. Friess WA, Rakhshan K. A review of passive envelope measures for improved building energy efficiency in the UAE. Renew Sustain Energy Rev [Internet]. 2017;72(November 2016):485–96. Available from: http://dx.doi.org/10.1016/j.rser.2017.01.026
    9.
  9. Kaynakli O. A review of the economical and optimum thermal insulation thickness for building applications. Renew Sustain Energy Rev. 2012;16(1):415–25.
    10.
  10. Yu J, Tian L, Yang C, Xu X, Wang J. Optimum insulation thickness of residential roof with respect to solar-air degree-hours in hot summer and cold winter zone of china. Energy Build [Internet]. 2011;43(9):2304–13. Available from: http://dx.doi.org/10.1016/j.enbuild.2011.05.012
    11.
  11. Mahmoudi M, Pakari N, Bahrami H. The effect of green roof on reducing environment temperature. Baghe-Nazar. 2012; 9(20); 3-12. (In Persian)
    12.
  12. Nasrollahi N, Salehi M. Performance enhancement of double skin facades in hot and dry climates using wind parameters. Renew Energy. 2015;83:1–12.
    13.
  13. Farhanieh B, Sattari S. Simulation of energy saving in Iranian buildings using integrative modelling for insulation. Renew Energy. 2006; 31(4):417–25.
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  14. Taheri M, Shafie S. A case study on the reduction of energy use for the heating of buildings. Renew Energy. 1995;6(7):673–8.
    15.
  15. 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. Appl Energy [Internet]. 2016;170:293–303. Available from: http://dx.doi.org/10.1016/j.apenergy.2016.02.141.
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    22.
  22. Shi X, Tian Z, Chen W, Si B, Jin X. A review on building energy ef fi cient design optimization rom the perspective of architects. Renew Sustain Energy Rev [Internet]. 2016;65:872–84. Available from: http://dx.doi.org/10.1016/j.rser.2016.07.050
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  23. Harkouss F, Fardoun F, Biwole P. Author ’ s Accepted Manuscript. J Build Eng [Internet]. 2017; Available from: https://doi.org/10.1016/j.jobe.2017.12.003
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