پیش‌بینی دمای سیال خروجی از جمع‌کننده خورشیدی صفحه تخت با دو روش شبکه عصبی مصنوعی (ANN) و تخمین گر بردار پشتیبان (SVR)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی مکانیک بیوسیستم، دانشگاه رازی، کرمانشاه، ایران

2 دانشیار، گروه مهندسی مکانیک بیوسیستم، دانشگاه رازی، کرمانشاه، ایران

3 استادیار، گروه مهندسی مکانیک بیوسیستم، دانشگاه رازی، کرمانشاه، ایران

4 استادیار، گروه مهندسی مکانیک بیوسیستم، دانشگاه ایلام، ایلام، ایران

چکیده

در مطالعه حاضر دمای آب خروجی از جمع کننده خورشیدی صفحه تخت با استفاده از شبکه‌های عصبی مصنوعی (ANN) و تخمین‌گر بردار پشتیبان (SVR) در دو حالت مدل و با نتایج تجربی مقایسه شد. نتایج نشان داد که با افزایش پارامترهای ورودی مدل‌ها، دقت مدل افزایش یافت. بر اساس نتایج  مقادیر R2، MSE و MAPE در روش SVRبرای مدل اول به ترتیب برابر 97/0، 25/3 و  77/2 و برای پارامترهای مدل دوم به ترتیب برابر 99/0، 10/0 و 55/0 به‌دست آمد. در حالی که این مقادیر برای روش ANN برای مدل اول به ترتیب برابر 99/0، 02/0 و 28/0، و برای مدل دوم به ترتیب برابر 99/0، 01/0 و 19/0 به دست آمد. نتایج نشان داد که مدل شبکه عصبی مصنوعی نسبت به مدل تخمین‌گر بردار پشتیبان با دقت بیشتری دمای آب خروجی از جمع‌کننده خورشیدی صفحه تخت را پیش بینی کرد.  

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Forecasting the Outlet Fluid Temperature from a Flat Plate Collector Using Artificial Neural Networks (ANNs) and Support Vector Regression (SVR)

نویسندگان [English]

  • Lida Dehlaghi 1
  • Hekmat Rabbani 2
  • Esmaeil Mirzaee- Ghaleh 3
  • Kamran Kheiralipour 4
1 MSc. Student, Mechanical Engineering of Biosystem Department, Razi University, Kermanshah, Iran
2 Associate Professor, Mechanical Engineering of Biosystem Department, Razi University, Kermanshah, Iran
3 Assistant Professor, Mechanical Engineering of Biosystem Department, Razi University, Kermanshah, Iran
4 Assistant Professor, Mechanical Engineering of Biosystem Department, Ilam University, Ilam, Iran
چکیده [English]

In the present study, the outlet water temperature from flat plate solar collector using artificial neural networks (ANNs) and support vector regression (SVR) was modeled and compared with experimental results. Based on the results, with increasing input parameters of models, the accuracy of the model was increased. According to the results the values ​​of R2, RMSE and MAPE in the SVR method for the first model were 0.97, 3.25 and 2.77, respectively. While these values for the second model was 0.99, 0.10 and 0.55, respectively. On the other hand, ​​for the ANN method and for the first model these values were 0.99 and 0.02 and 0.28, respectively. And for the second model were 0.99 and 0.01 and 0.19, respectively. The results showed that the accuracy of artificial neural network model for peridicting the water outlet temperature was better than that of the support vector regression model.

کلیدواژه‌ها [English]

  • Support Vector Regression
  • Solar collector
  • Water Temperature
  • artificial neural network
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