کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
262878 504052 2014 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
The artificial neural network model to estimate the photovoltaic modul efficiency for all regions of the Turkey
ترجمه فارسی عنوان
مدل شبکه عصبی مصنوعی برای تخمین بازده مولد فتوولتائیک برای تمام مناطق ترکیه
کلمات کلیدی
انرژی خورشیدی، فتوولتائیک، شبکه های عصبی مصنوعی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی


• PV module efficiency was studied experimentally.
• An ANN model used to estimate the PV module temperature and efficiency.
• The paper deals with for all region of Turkey.
• Photovoltaic power map was created for 81 city placed in Turkey.

Artificial neural network (ANN) is a useful tool that using estimates behavior of the most of engineering applications. In the present study, ANN model has been used to estimate the temperature, efficiency and power of the Photovoltaic module according to outlet air temperature and solar radiation. An experimental system consisted photovoltaic module, heating and cooling sub systems, proportional integral derivative (PID) control unit was designed and built. Tests were realized at the outdoors for the constant ambient air temperatures of photovoltaic module. To preserve ambient air temperature at the determined constant values as 10, 20, 30 and 40 °C, cooling and heating subsystems which connected PID control unit were used in the test apparatus. Ambient air temperature, solar radiation, back surface of the photovoltaic module temperature was measured in the experiments. Obtained data were used to estimate the photovoltaic module temperature, efficiency and power with using ANN approach for all 7 region of the Turkey. The study dealing with this paper not only will beneficial for the limited region but also in all region of Turkey which will be thought established of photovoltaic panels by the manufacturer, researchers and etc.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy and Buildings - Volume 84, December 2014, Pages 258–267
نویسندگان
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