کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5447118 | 1511145 | 2016 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Development of ANN Based Model for Solar Potential Assessment Using Various Meteorological Parameters
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Solar potential assessment is very useful for various applications like solar heating, agriculture, solar lighting system and solar power plant erection etc. The objective of the current study is to identify theoretical potential of solar radiation for solar energy applications in hilly state Himachal Pradesh. Artificial Neural Network (ANN) is used to predict solar radiation using site specific measured data of Hamirpur for training and testing. The input variables used are temperature, rainfall, sunshine hours, humidity &barometric pressure to predict solar radiations. To identify the effect of various input parameters on solar radiations three ANN based models have been developed represented by ANN-I5, ANN-I4& ANN-I3.To obtain best prediction result, the number of input parameters of the input layer have been varied between 3 to 5 and hidden layer neuron have also been varied between 10 to 20. The best mean absolute percentage error (MAPE) calculated for these models (ANN-I5, ANN-I4 & ANN-I3) are 16.45%, 18.77% and 19.39% respectively. The ANN-I5 (temperature, humidity, barometric pressure, rainfall and sun shine hours), model has shown good prediction accuracy as compared to other two models. This study shows that various numbers of meteorological parameters mostly affect the forecasting of solar radiation. The method in this paper can also be used to identify the solar energy potential of any location worldwide where it is not possible to install direct measuring instrument.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy Procedia - Volume 90, December 2016, Pages 587-592
Journal: Energy Procedia - Volume 90, December 2016, Pages 587-592
نویسندگان
Sanjay Kumar, Tarlochan Kaur,