Evaluating Potential of Artificial Neural Network and Neuro-Fuzzy Techniques for Global Solar Radiation Prediction in Isfahan, Iran

	Evaluating Potential of Artificial Neural Network and Neuro-Fuzzy Techniques for Global Solar Radiation Prediction in Isfahan, Iran
Journal of Agricultural Science and Technology
Article 5, Volume 21, Issue 2, 2019, Pages 295-307 PDF (786.56 K)
Authors
S. Taghadomi-Saberi^* ; S. J. Razavi
Department of Biosystem Engineering, College of Agriculture, Isfahan University of Technology, Islamic Republic of Iran.
Abstract
In this study, two widely used artificial intelligence techniques, i.e. Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), were applied for global solar radiation (GSR) prediction in Isfahan Province, Iran. Different sets of meteorological data were used as inputs to specify the best set of inputs. Relative humidity and precipitation had an unfavorable effect on radiation prediction, while the number of days, sunshine duration, minimum temperature, maximum temperature, daylight hours and clear-sky radiation were effective parameters to determine GSR. Using the mentioned parameters as inputs, 6-5-1 architecture had the best performance without overtraining. In ANFIS models, ' triangular-shaped' had the highest performance amongst different types of membership functions. Resulted correlation coefficients and errors showed that ANN was generally better than ANFIS for this purpose.
Keywords
ANFIS; ANN; GSR; Meteorological data; Modeling
References

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