Hourly global solar forecasting models based on a supervised machine learning algorithm and time series principle

Belaid, Sabrina; Mellit, A.; Boualit, Hamid; Zaiani, Mohamed

doi:10.1080/01430750.2020.1718754

Cited by 10 publications

(3 citation statements)

References 40 publications

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“… [ 66 ] Ghardaia, Algeria ARMA, NARX, and AR Historical solar radiation Hourly global solar radiation May 2013 to October 2013 RMSE, NRMSE, MAPE, NMBE and R NARX estimated the solar radiation data more accurately than other models. [ 67 ] …”

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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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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“…In fact, the radial basis function (RBF) has been widely used by researchers in the field of solar radiation forecasting [3,11,14,18]. For this reason, the RBF has been utilized as a kernel function whose expression is formulated as follows:…”

Section: Support Vector Machine Support Vector Machine (Svm) Was Inimentioning

confidence: 99%

“…e results obtained indicated the superiority of the proposed hybrid model as compared with conventional RFs and ANN models. Belaid et al [14] developed a new approach based on the SVM model and time series principle for forecasting 1h ahead of global solar radiation. e results showed a high accuracy of the proposed method using previous solar radiation values.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Model for Hourly Solar Radiation Forecasting Using Daily Classification Technique and Machine Learning Algorithms

Ali-Ou-Salah

Oukarfi

Bahani

et al. 2021

Mathematical Problems in Engineering

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Photovoltaic power generation depends significantly on solar radiation, which is variable and unpredictable in nature. As a result, the production of electricity from photovoltaic power cannot be guaranteed permanently during the operational phase. Forecasting global solar radiation can play a key role in overcoming this drawback of intermittency. This paper proposes a new hybrid method based on machine learning (ML) algorithms and daily classification technique to forecast 1 h ahead of global solar radiation in the city of Évora. Firstly, several comparative studies have been done between random forest (RF), gradient boosting (GB), support vector machines (SVM), and artificial neural network (ANN). These comparisons were made using annual, seasonal, and daily testing sets in order to determine the best ML algorithm under different meteorological conditions. Subsequently, the daily classification technique has been applied to classify the original training set into sunny and cloudy training subsets in order to enhance the forecasting accuracy. The evaluation of the proposed ML algorithms was carried out using the normalized root mean square error (nRMSE) and the normalized absolute mean error (nMAE). The results of the seasonal comparison show that the RF model performs well for spring and autumn seasons with nRMSE equaling 22.53% and 23.42%, respectively. While the SVR model gives good results for winter and summer seasons with nRMSE equaling 24.31% and 8.41%, respectively. In addition, the daily comparison demonstrates that the RF model performs well for cloudy days with nRMSE = 41.40%, while the SVR model yields good results for sunny days with nRMSE = 8.88%. The results show that the daily classification technique enhances the forecasting accuracy of ML models. Furthermore, this study demonstrates that the forecasting accuracy of ML algorithms depends significantly on sky conditions.

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