Estimation of daily global solar radiation using empirical and machine-learning methods: A case study of five Moroccan locations

Bounoua, Zineb; Chahidi, Laila Ouazzani; Mechaqrane, Abdellah

doi:10.1016/j.susmat.2021.e00261

Cited by 42 publications

(28 citation statements)

References 48 publications

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“…In order to assess the performance of the studied methods in predicting the desired outputs, three of the statistical indicators widely used in the literature are employed to compare the predicted and measured values: the coefficient of correlation ( R ), the normalized root mean square error ( nRMSE ) and the normalized mean absolute error ( nMAE ); these indicators are calculated based on Equations ( 13), ( 14) and ( 15) [22], respectively. Normalized values of MAE and RMSE are used to prevent the dataset scale dependency [23].…”

Section: Performance Analysismentioning

confidence: 99%

“…In this study, the nRMSE varies between 12.86% (prediction of the air conditioning electrical consumption in the period from mid-August to mid-September using the GPR model) to 24.72% (prediction of the PV module electrical production in the month of March using the Boosting trees method), as presented in Figure 10a. According to several studies [23][24][25], the selected models have acceptable to good performances (10% 30% nRMSE < < ). The nRMSE values varies from 12.96% to 22.05% for the ANN, from 12.86% to 21.50% for the GPR, from 15.48% to 23.34% for the SVM and from 13.83% to 24.72% for the Boosting trees method.…”

Section: Comparison and Analysismentioning

confidence: 99%

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Evaluation of Supervised Learning Models in Predicting Greenhouse Energy Demand and Production for Intelligent and Sustainable Operations

et al. 2021

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Plants need a specific environment to grow and reproduce in fine fettle. Nevertheless, climatic conditions are not stable and can impact their well-being and, consequently, harvest quality. Thus, greenhouse cultivation is one of the suitable agricultural techniques for creating and controlling the inside microclimate to be adequate for plant growth. The relevance of greenhouse control is widely recognized. The prediction of greenhouse variables using artificial intelligence methods is of great interest for intelligent control and the potential reduction in energetic and financial losses. However, the studies carried out in this context are still more or less limited and several machine learning methods have not been sufficiently exploited. The aim of this study is to predict the air conditioning electrical consumption and photovoltaic module electrical production at the smart Agro-Manufacturing Laboratory (SamLab) greenhouse, located in Albenga, north-western Italy. Different supervised machine learning methods were compared, namely, Artificial Neural Networks (ANNs), Gaussian Process Regression (GPR), Support Vector Machine (SVM) and Boosting trees. We evaluated the performance of the models based on three statistical indicators: the coefficient of correlation (R), the normalized root mean square error (nRMSE) and the normalized mean absolute error (nMAE). The results show good agreement between the measured and predicted values for all models, with a correlation coefficient R > 0.9, considering the validation set. The good performance of the models affirms the importance of this approach and that it can be used to further improve greenhouse efficiency through its intelligent control.

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Section: Performance Analysismentioning

confidence: 99%

Section: Comparison and Analysismentioning

confidence: 99%

Evaluation of Supervised Learning Models in Predicting Greenhouse Energy Demand and Production for Intelligent and Sustainable Operations

et al. 2021

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“…These methods have been successfully applied in many solar radiation related studies. Benouna et al [28] compared 3 tree-based models (boosted trees, bagged trees, and random forest (RF)), 22 empirical models, and an MLP model, for estimating H in five locations in Morocco. Their results revealed the superiority of the (RF) model in terms of r, normalized mean absolute error (nMAE), and (nRMSE) that were in the range of 0.8753-0.9620, 5.84-11.81%, and 7.85-15.33%, respectively.…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction

et al. 2021

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Machine learning (ML) models are commonly used in solar modeling due to their high predictive accuracy. However, the predictions of these models are difficult to explain and trust. This paper aims to demonstrate the utility of two interpretation techniques to explain and improve the predictions of ML models. We compared first the predictive performance of Light Gradient Boosting (LightGBM) with three benchmark models, including multilayer perceptron (MLP), multiple linear regression (MLR), and support-vector regression (SVR), for estimating the global solar radiation (H) in the city of Fez, Morocco. Then, the predictions of the most accurate model were explained by two model-agnostic explanation techniques: permutation feature importance (PFI) and Shapley additive explanations (SHAP). The results indicated that LightGBM (R2 = 0.9377, RMSE = 0.4827 kWh/m2, MAE = 0.3614 kWh/m2) provides similar predictive accuracy as SVR, and outperformed MLP and MLR in the testing stage. Both PFI and SHAP methods showed that extraterrestrial solar radiation (H0) and sunshine duration fraction (SF) are the two most important parameters that affect H estimation. Moreover, the SHAP method established how each feature influences the LightGBM estimations. The predictive accuracy of the LightGBM model was further improved slightly after re-examination of features, where the model combining H0, SF, and RH was better than the model with all features.

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“…The accuracy of the results was tested by Mean Absolute Percentage Error percentage (MAPE) with 7.3%. In Morocco, Bounoua et al (2021) have used the neural network method, 22 empirical models, and tree-based ensemble methods to estimate the daily GSR in five studies locations. In terms of accuracy, the proposed methods were evaluated using R 2 , NRMSE, and NMAE.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Global Solar Radiation Prediction in 25 Cities in Morocco Using the FFNN-BP Method

et al. 2021

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This article presents different combinations of input parameters based on an intelligent technique, using neural networks to predict daily global solar radiation (GSR) for twenty-five Moroccan cities. The collected measured data are available for 365 days and 25 stations around Morocco. Different input parameters are used, such as clearness index KT, day number, the length of the day, minimal temperature Tmin, maximal temperature Tmax, average temperature Taverage, difference temperature ΔT, ratio temperature T-Ratio, average relative humidity RH, solar radiation at the top outside atmosphere TOA, average wind speed Ws, altitude, longitude, latitude, and solar declination. A different combination was employed to predict daily GSR for the considered locations in order to find the most adequate input parameter that can be used in the prediction procedure. Several statistical metrics are applied to evaluate the performance of the obtained results, such as coefficients of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), test statistic (TS), linear regression coefficients (the slope “a” and the constant “b”), and standard deviation (σ). It is found that the usage of input parameters gives highly accurate results in the artificial neural network (FFNN-BP) model, obtaining the lowest value of the statistical metrics. The results showed the best input of 25 locations, 12 inputs for Er-Rachidia, Marrakech, Medilt, Taza, Oujda, Nador, Tetouan, Tanger, Al-Auin, Dakhla, Settat, and Safi, seven inputs for Fes, Ifrane, Beni-Mellal, and Meknes, six inputs for Agadir and Rabat, five inputs for Sidi Ifni, Essaouira, Casablanca and Kenitra, four inputs for Ouarzazate, Lareche, and Al-Hoceima. In terms of accuracy, R2 of the selected best inputs parameters varies between 0.9860% and 0.9920%, the range value of MBE (%) being from −0.1076% to −0.5931%, the RMSE between 0.1990 and 0.4580%, the range value of the NRMSE between 0.0355 and 0.8938, and the lowest value MAPE between 0.0019 and 0.0060%. This technique could be used to predict other parameters for locations where measurement instrumentation is unavailable or costly to obtain.

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Estimation of daily global solar radiation using empirical and machine-learning methods: A case study of five Moroccan locations

Cited by 42 publications

References 48 publications

Evaluation of Supervised Learning Models in Predicting Greenhouse Energy Demand and Production for Intelligent and Sustainable Operations

Evaluation of Supervised Learning Models in Predicting Greenhouse Energy Demand and Production for Intelligent and Sustainable Operations

An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction

Long-Term Global Solar Radiation Prediction in 25 Cities in Morocco Using the FFNN-BP Method

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