Application of machine learning for solar radiation modeling

Taki, Morteza; Rohani, Abbas; Yıldızhan, Hasan

doi:10.1007/s00704-020-03484-x

Cited by 22 publications

(11 citation statements)

References 61 publications

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“…In other words, MLR aims to find the linear function that minimizes the sum of the squares of errors (SSE) between the observed and the predicted data. An advantage of this method is the easy interpretation of the coefficients, which are generated in the model with low computational effort, in comparison to more complex techniques, such as energy balance methods and artificial intelligence algorithms [13][14][15][16][17][18][19][20][21][24][25][26][27][28][29][30][37][38][39][40][41][42][43][67][68][69][70][71][72][73][74][75]. For the MLR model, the response (dependent) variable y is assumed to be a function of k independent variables x i .…”

Section: Methodsmentioning

confidence: 99%

“…Among the highly complex methods that have been tested lately, multiple linear regression (MLR) has been suggested by a number of recent studies as an adequate method for estimating climatic parameters such as air temperature, growing degree days, and solar radiation [41][42][43][44]. Remotely sensed data, especially MODIS and Landsat products, have gained ground in MLR modelling lately, as inputs for the estimation of climatic parameters, such as land surface temperature and surface urban heat island [45][46][47].…”

Section: Introductionmentioning

confidence: 99%

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Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece

Dimitriadou

Nikolakopoulos

2022

Hydrology

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The aim of this study was to investigate the utility of multiple linear regression (MLR) for the estimation of reference evapotranspiration (ETo) of the Peloponnese, Greece, for two representative months of winter and summer during 2016–2019. Another objective was to test the number of inputs needed for satisfactorily accurate estimates via MLR. Datasets from sixty-two meteorological stations were exploited. The available independent variables were sunshine hours (N), mean temperature (Tmean), solar radiation (Rs), net radiation (Rn), wind speed (u2), vapour pressure deficit (es − ea), and altitude (Z). Sixteen MLR models were tested and compared to the corresponding ETo estimates computed by FAO-56 Penman–Monteith (FAO PM) in a previous study, via statistical indices of error and agreement. The MLR5 model with five input variables outperformed the other models (RMSE = 0.28 mm d−1, adj. R2 = 98.1%). Half of the tested models (two to six inputs) exhibited very satisfactory predictions. Models of one input (e.g., N, Rn) were also promising. However, the MLR with u2 as the sole input variable presented the worst performance, probably because its relationship with ETo cannot be linearly described. The results indicate that MLR has the potential to produce very good predictive models of ETo for the Peloponnese, based on the literature standards.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece

Dimitriadou

Nikolakopoulos

2022

Hydrology

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“…In this article, the authors used three performance functions to analyze the target and output accuracy. These functions include the correlation coefficient R, the root mean squared error (RMSE), and the coefficient of determination

R^{2}

, 52–55 which are defined as follows:

R = \frac{y_{o} (i)}{y_{e} (i)}

RMSE = \sqrt{\frac{\sum_{i = 1}^{n} {(y_{e} (i) - y_{o} (i))}^{2}}{n}}

R^{2} = {([], \frac{\sum_{i = 1}^{n} (y_{e} (i) - \overset{true¯}{y_{e}}) (y_{o} (i) - \overset{true¯}{y_{0}})}{\sum_{i = 1}^{n} (y_{e} (i) - \overset{true¯}{y_{e}}) \sum_{i = 1}^{n} (y_{o} (i) - \overset{true¯}{y_{0}})})}^{2}

where “ y e ” and “ y 0 ” are, respectively, the estimated and the observed output, and

\overset{true¯}{y_{e}}

and

\overset{true¯}{y_{0}}

represent their mean values.…”

Section: Back Propagation Neural Network For Desalination‐renewable E...mentioning

confidence: 99%

Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

Zgalmi

Cherif

2022

Env Prog and Sustain Energy

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In this article, smart water-energy management based on an artificial neural network for a photovoltaic-wind system coupled with water pumping and reverse osmosis desalination unit with hydraulic storage is developed. The proposed system was designed to meet freshwater demand for remote areas of Tunisia's south. The authors focus on an artificial neural network-type management strategy, ensuring power-sharing between sources, hydraulic components, and tank storage. The main objective of the management strategy is to deliver and purify as much freshwater as possible by taking advantage of all the energy available according to climatic conditions. A parametric sensitivity algorithm is developed to find the most appropriate neural network architecture. A dynamic simulator for 1 year of operation integrates the energy management of the system is developed. The used water-energy management-based artificial neural network showed good results of power-sharing during a fast time with a correlation coefficient and a coefficient of determination between the actual and estimated values of the three motor pumps electrical power equal to 96% and 98%, respectively. Also, the root mean squared error was acceptable at 0.1476. These results have been proved over a year and can be used by other researchers with a similar system.

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“…Also, their results revealed that the accuracy of the predictions depends on the used category, training algorithm, and variable combinations. Taki et al [21] utilized ANN, SVR, Adaptive Network-Based Fuzzy Inference System (ANFIS), Radial Basis Function (RBF), and Multiple Linear Regression (MLR) for Estimating GSR at different time scales. They concluded that the RBF-based model has the lowest error when estimating the monthly and daily solar irradiations.…”

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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Application of machine learning for solar radiation modeling

Cited by 22 publications

References 61 publications

Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece

Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece

Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

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

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