Solar irradiation estimations and comparisons by ANFIS, Angström–Prescott and dependency models

Güçlü, Yavuz Selim; Yeleğen, Mehmet Öner; Dabanlı, İsmail; Şişman, Eyüp

doi:10.1016/j.solener.2014.08.027

Cited by 37 publications

(13 citation statements)

References 26 publications

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“…Mohanty et al (2015) carried out a comparative study among three soft computing methodologies including ANFIS, multilayer perceptron (MLP), and radial basis function (RBF) for prediction of monthly mean global solar radiation in three locations of India. Güçlü et al (2014) performed an investigation to apply the ANFIS model, Angstr m-Prescott model, and some proposed models named dependency models for estimating global solar radiation in Turkey using sunshine duration as input.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model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

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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.

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Section: Introductionmentioning

confidence: 99%

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

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“…8-12 and Table 6. Based on the research methods of Gü çlü et al (2014), comparison between calculation value of models and measured value shows that, on the whole, the fitted value of the DGSRD model 2 and DGSRD model 4 are the most close to measured value, and the accuracy is relatively high, followed by DGSRD model 3. The deviation between the fitted values of the DGSRD model 1 and CPRG model and measured values of the two models are relatively large, its accuracy is relatively low.…”

Section: Resultsmentioning

confidence: 98%

New decomposition models to estimate hourly global solar radiation from the daily value

Yao

Xiu

et al. 2015

Solar Energy

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“…The first step for the HarLin model is to get arithmetic average for each day. The last row in this table includes 365 daily [12]. Table 1 Calculation of daily averages.…”

Section: Harlin Methodologymentioning

confidence: 99%

HARmonic–LINear (HarLin) model for solar irradiation estimation

et al. 2015

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a b s t r a c tThe solar energy has potential future effectiveness in a variety of areas and it helps to decrease the dominance of fossil fuels, which cause atmospheric pollution, global warming and climate change impacts. In the literature, there are different methodologies for its modeling, but this study suggests the harmonic analysis application to solar irradiation and sunshine duration data for more refined relevant prediction of solar irradiation. The basis of the methodology is combined application of the HARmonic and the classical LINear regression analyses, and therefore, it is referred to as the HarLin model. It isolates first the periodicity from the daily averages of records and then the linear regression analysis is applied elegantly to first order stationary data. The results are tested and compared with the Adaptive-Neuro Fuzzy Inference System (ANFIS) model based on the Sugeno fuzzy logic inference system and Angstr€ omePrescott model in the form of a linear regression analysis. In the application, three solar irradiation sites are considered from different solar energy potential locations in Turkey, namely, at Adana, Gaziantep and Silifke cities. The predictions by the HarLin model appear more successful than ANFIS and the classical Angstr€ omePrescott approaches.

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Solar irradiation estimations and comparisons by ANFIS, Angström–Prescott and dependency models

Cited by 37 publications

References 26 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

New decomposition models to estimate hourly global solar radiation from the daily value

HARmonic–LINear (HarLin) model for solar irradiation estimation

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