A comparative study of estimating solar radiation using machine learning approaches: DL, SMGRT, and ANFIS

Üstün, İsmail; Üneş, Fatih; Mert, İlker; Karakuş, Cuma

doi:10.1080/15567036.2020.1781301

Cited by 19 publications

(5 citation statements)

References 49 publications

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“…The ANFIS model is one of the most efficient AI methods that has been used in both simple and hybridized forms for hydrological and meteorological modeling. ANFIS model showed its acceptable performances in solar radiation estimation (Üstün et al 38 ; Halabi et al 34 ; Khosravi et al 39 ), pan evaporation estimation (Adnan et al 40 ; Guven and Kisi 41 ), drought forecasting (Aghelpour et al 42 ; Aghelpour et al 43 ; Aghelpour et al 44 ; Kisi et al 45 ), river flow modeling (Mohammadi et al 46 ; Aghelpour et al 47 ), rainfall forecasting (Mekanik et al 48 ; Yaseen et al 49 ), and wind speed forecasting (Maroufpoor et al 50 ). However, they are less used in evapotranspiration prediction for the future (most of the studied cases have used the ANFIS model for ET0 “estimation,” not “prediction” for the future).…”

Section: Introductionmentioning

confidence: 99%

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Aghelpour

Varshavian

Pour

et al. 2022

Sci Rep

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Evapotranspiration is one of the most important hydro-climatological components which directly affects agricultural productions. Therefore, its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly reference evapotranspiration (ET0). Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on a monthly scale during 1995–2018, based on FAO-56 Penman–Monteith equation and meteorological data including minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid), and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE), and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models and superior to its competitors, ANFIS and ANFIS-DE. Consequently, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates, due to its simplicity and parsimony. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

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Section: Introductionmentioning

confidence: 99%

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Aghelpour

Varshavian

Pour

et al. 2022

Sci Rep

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“… [ 82 ] Isparta, Türkiye DL, SMGRT, and ANFIS Soil, and air temperature sunshine duration, relative humidity, cloudiness, and extraterrestrial solar radiation Monthly global solar radiation 2007 to 2016 MBE, MSE, RMSE, and R 2 Each model presented very satisfied results in predicting the GSR, but SMGRT comes to the fore according to the statistical metrics. [ 83 ] Tuscaloosa, Alabama in the USA ANN, and RNN Wind speed, dew-point temperature, outdoor air-dry bulb temperature, relative humidity, and wind direction Daily global solar radiation January 14, 2019, to January 21, 2019 RMSE, NMBE CV(RMSE), and R 2 Cloud cover was a vital effect on the prediction of GSR. RNN had better prediction results, but it had 800 times higher computational costs than ANN.…”

Section: Literature Reviewmentioning

confidence: 99%

A state of art review on estimation of solar radiation with various models

Gürel¹,

Ağbulut²,

Bakır³

et al. 2023

Heliyon

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“…The input parameters were wind speed, temperature, pressure, and humidity; statistically, the gaussian model performed better. Various machine learning algorithms based on linear and nonlinear regression approaches were also examined [24][25][26][27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Approach for Solar Irradiance Estimation on Tilted Surfaces in Comparison with Sky Models Prediction

Mbah

Madueke

Umunakwe

et al. 2022

JES

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In this study, two supervised machine learning models (Extreme Gradient Boosting and K-nearest Neighbour) and four isotropic sky models (Liu and Jordan, Badescu, Koronakis, and Tian) were employed to estimate global solar radiation on daily data measured for one year period at the National Center for Energy, Research and Development (NCERD) at the University of Nigeria, Nsukka. Two solarimeters were employed to measure solar radiation: one measured solar radiation on a tilted surface at a 15° angle of tilt, facing south, and the other measured global horizontal solar radiation. The measured global horizontal solar radiation and the time and day number were used as input for the prediction process. Python computational software was used for model prediction, and the performance of each model was assessed using statistical methods such as mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE) (RMSE). Compared to the measured data, it was discovered that the Extreme Gradient Boosting (XGBoost) algorithm oﬀered the best performance with the least inaccuracy to sky models.

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A comparative study of estimating solar radiation using machine learning approaches: DL, SMGRT, and ANFIS

Cited by 19 publications

References 49 publications

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

A state of art review on estimation of solar radiation with various models

Machine Learning Approach for Solar Irradiance Estimation on Tilted Surfaces in Comparison with Sky Models Prediction

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