Application of Neuro-Fuzzy Techniques for Solar Radiation

Abstract

Problem statement:The prediction is very useful in solar energy applications because it permits to estimate solar data for locations where measurements are not available. The developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. Approach: The forecasting ability of some models could be further enhanced with the use of additional meteorological parameters. After having simulated many different structures of neural networks and trained using measurements as training data, the best structures were selected in order to evaluate their performance in relation with the performance of a neuro-fuzzy system. As the alternative system, ANFIS neuro-fuzzy system was considered, because it combines fuzzy logic and neural network techniques that are used in order to gain more efficiency. ANFIS is trained with the same data. Results: The comparison and the evaluation of both of the systems were done according to their predictions, using several error metrics. Fuzzy model was trained using data of daily solar radiation recorded on a horizontal surface in National Research Institute of Astronomy and Geophysics, Helwan, Egypt (NARIG) at ten years (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000). Conclusion: The predicting conclusion indicated that the TS fuzzy model gave a good accuracy of approximately 96% and a root mean square error lower than 6%.

A review on classification and comparison of different models in solar radiation estimation

“…The results showed that ANN and ANFIS intelligent models are the powerful tools in prediction of GSR for the selected stations, but prediction by ANN was found to be more accurate than ANFIS [34]. Rahoma et al (2011) used ANFIS for SR estimation. They can prove that the combination of linguistic rules of FL with the training algorithm used in neural networks contributes in very qualitative prediction results, which approach the 'best' neural predictor's results [61].…”

“…Rahoma et al (2011) used ANFIS for SR estimation. They can prove that the combination of linguistic rules of FL with the training algorithm used in neural networks contributes in very qualitative prediction results, which approach the 'best' neural predictor's results [61].…”

Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran

“…In fact, these models presume that maximum temperature will decrease with reduced transmissivity, whilst minimum temperature will increase because of the cloud emissivity. Clear skies will increase maximum temperature due to higher shortwave radiation during sunshine hours, and minimum temperature will decrease due to higher transmissivity at night times; so the difference between daily maximum and minimum air temperatures becomes an indicator of cloudiness (Almorox et al, 2013) Estimating the horizontal global solar radiation using different artificial and computational intelligence techniques including the artificial neural network (ANN) (Tymvios et al 2005;Mubiru and Banda 2008;Şenkal and Kuleli 2009;Solmaz and Ozgoren, 2012), particle swarm optimization (PSO) (Mohandes, 2012), adaptive neuro-fuzzy inference system (ANFIS) (Mellit et al 2007;Rahoma et al, 2011;Sumithira and Kumar, 2012;Güçlü et al, 2014;Mohanty et al, 2015;Mohammadi et al, 2015a), support vector machine (SVM) (Chen et al, 2013;Mohammadi et al, 2015b;Mohammadi et al, 2015c), and etc. has received tremendous attention in recent years.…”

Soft Computing Approach to Predict Intracranial Pressure Values

“…In addition, the excessive computational load of the usual statistical predictiontechniques hardly find acceptance within the technology transfer for the production of devices hospital monitoring.In recent years, the soft computing prediction techniques have been worked out with excellent results both in theoretical and applicative fields. In particular, remarkable results have been obtained by fuzzy and neuro-fuzzy techniques (Rahoma et al, 2011) by means of the construction of banks of naive fuzzy rules or with more sophisticated techniques of extraction of inferences directly from numerical data with the undoubted advantage of creating linguistic structures easily modifiable and/or updateable by the knowledge of the expert. However, since ICP depends on several patho-physiological factors, the structuring of the banks of fuzzy rules may be often complicated with obvious problem of explosion of the number of the rules for which it is necessary to drastically reduce the number of inputs.…”