Potential of four different machine-learning algorithms in modeling daily global solar radiation

Hassan, Muhammed A.; Khalil, A.; Kaseb, S.; Kassem, Mohamed A.

doi:10.1016/j.renene.2017.03.083

Cited by 59 publications

(8 citation statements)

References 29 publications

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“…Abundant energy potential from solar radiation play an important role in meeting the growing world energy demand (Akikur, Saidur, Ping, & Ullah, 2013;Azoumah, Yamegueu, Ginies, Coulibaly & Girard, 2011;Ming, De_Richter, Liu & Caillol, 2014). Solar energy has attracted enormous attention not only because it is sustainable but also abundant and environmental friendly (Akikur et al, 2013;Hassan, Khalil, Kaseb, & Kassem, 2017).…”

Section: Introductionmentioning

confidence: 99%

Predictions of Daily Horizontal Solar Radiation for Rural Development: The Case of Mubi Town, Adamawa State, Nigeria

Luqman

Amusat

Anjorin

et al. 2020

IJATR

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The solar radiation which is produced by the sun has created interests for the renewable researchers in the area of solar technology to make environment friendly and develop communities through this abundant energy potential. This study developed a model of Daily Horizontal Solar Radiation (DHSR) for Mubi, Adamawa State, Nigeria. The present study data were obtained from National Aeronautics and Space Administration (NASA) in year 2017. The sought data were air temperature and Relative Humidity which were used as the input parameters and DHSR used as output. Mathematical Model of DHSR of the study area was generated through the Multiple Linear Regression (MLR) analysis that was done in the Microsoft World Excel. Results of the research were validated using statistical tools of Mean Square Error and Correlation Coefficient given 1.61 and 0.95 respectively. Consequently, it was concluded that MLR Model can be considered as a substitute for evaluating the Actual DHSR for weather condition data of Mubi. Therefore, this finding is significant to the development of the socio-economic activities of the Mubi Town. Based on this finding it was recommended that the behavious of MLR Model should be tested in other locations across the Northeast, Nigeria.

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Section: Introductionmentioning

confidence: 99%

Predictions of Daily Horizontal Solar Radiation for Rural Development: The Case of Mubi Town, Adamawa State, Nigeria

Luqman

Amusat

Anjorin

et al. 2020

IJATR

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“…The low recorded bias in most of the models is related to the good estimation of GHI by ANN models as mentioned in several studies [12,17,62,64]. We presented the overall bias among stations, which led to a decrease in the bias because of positive bias in some stations and negative bias in others in the same model, whereas the bias in all individual stations was lower than 2% except one case of 2.2% (Tables A1-A9, Figure A1).…”

Section: Discussionmentioning

confidence: 54%

“…Therefore, several studies have tried to estimate GHI empirically from the early 20th century until now from other climate variables, namely, Sunshine Duration (SD), Air Temperature (AT), cloud cover, and other variables, using the top-of-atmosphere irradiance on the horizontal surface (TOA) [6][7][8][9][10][11] and with linear regression models [12][13][14]. Recently, machine learning approaches have also been broadly used [15,16], which mostly include Artificial Neural Networks (ANNs), which will be discussed in a later section, Support Vector Machines, Random Forest [5,17,18] and some other machine learning models [19,20]. Some of these and other approaches have used satellite image data and interpolation techniques to cover the limitation of spatial resolution [3,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, geographical and meteorological parameters at different time scales as various inputs have been used with ANN models for a variety of climate regions and countries. For example, two cities in Oman [51]; eight cities [52] and nine cities [53] in China; 195 cities in Nigeria [54]; 27 stations [55], seven cities [56] and 30 cities [57] in Turkey; five stations in Argentina [12]; six cities in the Yucatan peninsula, Mexico [18]; five cities in Italy [58]; four cities in the USA; and two cities [59], 10 cities [60] and 12 cities in Iran [61]; and Cairo city in Egypt [17]. Generally, the results of those models in the literature show good agreement with ground data.…”

Section: Introductionmentioning

confidence: 99%

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Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

et al. 2019

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More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificial neural network with the Levenberg–Marquardt training algorithm to obtain results from training, validation and test data. It was applied at two station types in northeast Iraq. The test data results with observed input variables (correlation coefficient (r) = 0.755, Root Mean Square Error (RMSE) = 33.7% and bias = 0.3%) are improved with new input combinations for all variables (r = 0.983, RMSE = 9.5% and bias = 0.0%) at four automatic stations. Similarly, they improved at five tower stations with no recorded SD (from: r = 0.601, RMSE = 41% and bias = 0.7% to: r = 0.976, RMSE = 11.2% and bias = 0.0%). The estimation of hourly GHI is slightly enhanced by using the new inputs.

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“…They concluded that the RBF-based model has the lowest error when estimating the monthly and daily solar irradiations. Hassan et al [22] performed a comparative study between four different data-driven models of daily global solar irradiation based on SVR, feedforward backpropagation ANN, ANFIS, and decision trees. They showed that ANN models, followed by ANFIS-and SVM-based models, outperform classic regression-based ones by reducing the root mean square errors by up to 31.7%, depending on the type of the inputs.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

Jamei¹,

Bailek²,

Bouchouicha³

et al. 2023

Computers, Materials &Amp; Continua

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Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR), Random Subspace (RS), and M5P. The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation. The testing phase outcomes demonstrated that the AR-RF models outperform all other hybrid models. The provided models were validated by statistical metrics, such as the correlation coefficient (R) and root mean square error (RMSE). The results proved that Scenario #6, utilizing extraterrestrial solar radiation, relative humidity, wind speed, and mean, maximum, and minimum ambient air temperatures as the model inputs, leads to the most accurate predictions among all scenarios (R = 0.968-0.988 and RMSE = 1.274-1.403 MJ/m 2 •d). Also, Scenario #3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations. The AD-RF model was the best predictive, followed by AD-RS and AD-LWLR.

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Potential of four different machine-learning algorithms in modeling daily global solar radiation

Cited by 59 publications

References 29 publications

Predictions of Daily Horizontal Solar Radiation for Rural Development: The Case of Mubi Town, Adamawa State, Nigeria

Predictions of Daily Horizontal Solar Radiation for Rural Development: The Case of Mubi Town, Adamawa State, Nigeria

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

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