A computation ANN model for quantifying the global solar radiation: A case study of Al-Aqabah-Jordan

7th International Conference on Cooling & Heating Technologies

材料科学;物理学

Abolgasem, I.M.^1 ; Alghoul, M.A.^1 ; Ruslan, M.H.^1 ; Chan, H.Y.^1 ; Khrit, N.G.^1 ; Sopian, K.^1

Solar Energy Research Institute, Univeristi Kebangsaan Malaysia Bangi, Selangor DE

43600, Malaysia^1

关键词: Air temperature;    Computation model;    Global solar radiation;    Global solar radiation (GSR);    Meteorological station;    Prediction model;    Sunshine duration;    Training and testing;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/88/1/012073/pdf
DOI  :  10.1088/1757-899X/88/1/012073

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

In this paper, a computation model is developed to predict the global solar radiation (GSR) in Aqaba city based on the data recorded with association of Artificial Neural Networks (ANN). The data used in this work are global solar radiation (GSR), sunshine duration, maximum & minimum air temperature and relative humidity. These data are available from Jordanian meteorological station over a period of two years. The quality of GSR forecasting is compared by using different Learning Algorithms. The decision of changing the ANN architecture is essentially based on the predicted results to obtain the best ANN model for monthly and seasonal GSR. Different configurations patterns were tested using available observed data. It was found that the model using mainly sunshine duration and air temperature as inputs gives accurate results. The ANN model efficiency and the mean square error values show that the prediction model is accurate. It is found that the effect of the three learning algorithms on the accuracy of the prediction model at the training and testing stages for each time scale is mostly within the same accuracy range.