Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters

Rizwan, Mohammad; Jamil, Majid; Kirmani, Sheeraz; Kothari, D.P.

doi:10.1016/j.energy.2014.04.057

Cited by 81 publications

(22 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, some researchers have employed soft computing methodologies for global solar radiation prediction in various locations (Mohandes 2012;Rizwan et al 2014;Chen and Li 2014;Mohammadi et al 2015a,b).…”

Section: Introductionmentioning

confidence: 98%

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Gani

Mohammadi

Shamshirband

et al. 2015

Theor Appl Climatol

View full text Add to dashboard Cite

The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neurofuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44-0.61 kWh/m 2 , 0.50-0.71 kWh/m 2 , and 0.78-0.91, respectively.

show abstract

Section: Introductionmentioning

confidence: 98%

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Gani

Mohammadi

Shamshirband

et al. 2015

Theor Appl Climatol

View full text Add to dashboard Cite

show abstract

“…Their results indicated that SVM models show remarkable superiority over empirical models with an average of 14 % more precision. Rizwan et al (2014) applied fuzzy logic (FL) technique to model monthly mean global solar radiation in four Indian stations by different input data. They found that the developed FLbased model is accurate since the amounts of obtained errors are limited.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

Shamshirband

Mohammadi

Chong

et al. 2015

Theor Appl Climatol

View full text Add to dashboard Cite

In this study, a hybrid support vector machine-firefly optimization algorithm (SVM-FFA) model is proposed to estimate monthly mean horizontal global solar radiation (HGSR). The merit of SVM-FFA is assessed statistically by comparing its performance with three previously used approaches. Using each approach and long-term measured HGSR, three models are calibrated by considering different sets of meteorological parameters measured for Bandar Abbass situated in Iran. It is found that the model (3) utilizing the combination of relative sunshine duration, difference between maximum and minimum temperatures, relative humidity, water vapor pressure, average temperature, and extraterrestrial solar radiation shows superior performance based upon all approaches. Moreover, the extraterrestrial radiation is introduced as a significant parameter to accurately estimate the global solar radiation. The survey results reveal that the developed SVM-FFA approach is greatly capable to provide favorable predictions with significantly higher precision than other examined techniques. For the SVM-FFA (3), the statistical indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination (R 2 ) are 3.3252 %, 0.1859 kWh/m 2 , 3.7350 %, and 0.9737, respectively which according to the RRMSE has an excellent performance. As a more evaluation of SVM-FFA (3), the ratio of estimated to measured values is computed and found that 47 out of 48 months considered as testing data fall between 0.90 and 1.10. Also, by performing a further verification, it is concluded that SVM-FFA (3) offers absolute superiority over the empirical models using relatively similar input parameters. In a nutshell, the hybrid SVM-FFA approach would be considered highly efficient to estimate the HGSR.

show abstract

“…These models are the popular models in which the author(s) compared their new proposed models with others [33]. For instance, Angstrom-Prescott Model relates the global solar radiation with sunshine hours as given in (21) while Garcial Model described by (22) relates the difference between the maximum and minimum temperature to the global solar radiation of a location. The latter is useful where sunshine hour data is not available.…”

Section: Comparison Of the Proposed Methods To The Existing Models Formentioning

confidence: 99%

“…Fuzzy logic approach has also been used for modeling and estimating the global solar energy using mean duration sunshine per hour, temperature, latitude, longitude, altitude and months of the year as input parameters [22]. Extensively, FG (Fuzzy Genetic) hybrid approach has been considered for modelling solar radiation [23].…”

Section: Introductionmentioning

confidence: 99%

Prediction of monthly average global solar radiation based on statistical distribution of clearness index

Ayodele

Ogunjuyigbe

2015

Energy

View full text Add to dashboard Cite

Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters

Cited by 81 publications

References 15 publications

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

RETRACTED ARTICLE: A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

Prediction of monthly average global solar radiation based on statistical distribution of clearness index

Contact Info

Product

Resources

About