Open AccessThe Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
Author(s) -
Muna A Alzukrah,
Yosof M. Khalifa
Publication year - 2021
Publication title -
al-ṭāqaẗ al-s̆amsiyyaẗ wa-al-tanmiyyaẗ al-mustadāmaẗ/solar energy and sustainable development
Language(s) - English
Resource type - Journals
eISSN - 2414-6013
pISSN - 2411-9636
DOI - 10.51646/jsesd.v5i2.83
Subject(s) - adaptive neuro fuzzy inference system , mean squared error , mean absolute percentage error , fist , artificial neural network , neuro fuzzy , latitude , meteorology , fuzzy logic , statistics , computer science , environmental science , data mining , mathematics , machine learning , artificial intelligence , fuzzy control system , geography , geodesy , physiology , biology
The prediction of solar radiation is very important tool in climatology, hydrology and energy applications, as it permits estimating solar data for locations where measurements are not available. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is presented to predict the monthly global solar radiation on a horizontal surface in Libya. The real meteorological solar radiation data from 5 stations for the period of 1982 - 2009 with diffrent latitudes and longitudes were used in the current study. The data set is divided into two subsets; the fist is used for training and the latter is used for testing the model. (ANFIS) combines fuzzy logic and neural network techniques that are used in order to gain more effiency. The statistical performance parameters such as root mean square error (RMSE), mean absolute percentage error (MAPE) and the coeffient of effiency (E) were calculated to check the adequacy of the model. On the basis of coeffient of effiency, as well as the scatter diagrams and the error modes, the predicted results indicate that the neuro-fuzzy model gives reasonable results: accuracy of about 92% - 96% and the RMSE ranges between 0.22 - 0.35 kW.hr/m2/day