Reliable solar irradiance prediction using ensemble learning-based models: A comparative study

Lee, Junho; Wu, Wei; Harrou, Fouzi; Sun, Ying

doi:10.1016/j.enconman.2020.112582

Cited by 106 publications

(60 citation statements)

References 39 publications

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“…GPR is an important kernel-based learning algorithm that has attracted attention in modeling multivariate data [31], [35], [36]. Essentially, GPR is an efficient tool for exploring implicit relationships between multiple process variables based on training data, which makes GPR particularly helpful in dealing with challenging nonlinear regression.…”

Section: ) Gaussian Process Regression (Gpr)mentioning

confidence: 99%

Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes

Harrou

Saidi

Sun

et al. 2021

IEEE J. Photovoltaics

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Data-based procedures for monitoring the operating performance of a PV system are proposed in this paper. The only information required to apply the procedures is the availability of system measurements, which are routinely on-line collected via sensors. Here, kernel-based machine learning methods, including support vector regression (SVR) and Gaussian process regression (GPR), are used to model multivariate data from the PV system for fault detection due to their flexibility and capability to nonlinear approximation. Essentially, the SVR and GPR models are adopted to obtain residuals to detect and identify occurred faults. Then, residuals are passed through an exponential smoothing filter to reduce noise and improve data quality. In this work, a monitoring scheme based on kernel density estimation is used to sense faults by examining the generated residuals. Several different scenarios of faults were considered in this study, including PV string fault, partial shading, PV modules shortcircuited, module degradation, and line-line faults on the PV array. Using data from a 20 MWp grid-connected PV system, the considered faults were successfully traced using the developed procedures. Also, it has been demonstrated that GPR-based monitoring procedures achieve better detection performance over SVRs to monitor PV systems.

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Section: ) Gaussian Process Regression (Gpr)mentioning

confidence: 99%

Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes

Harrou

Saidi

Sun

et al. 2021

IEEE J. Photovoltaics

Self Cite

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“…In addition, the GB algorithm is viewed as a gradient descendent algorithm for regression problems in some function space. It evaluates the shortcoming of weak learners by using the gradients in the loss function [27]. In fact, the most common learner is the regression tree, which is weak as an individual learner.…”

Section: Gradient Boosting Gradient Boosting (Gb) Algorithmmentioning

confidence: 99%

“…where θ t is a parameter vector, Y j and R j are, respectively, the mean and separated input space of the J-th terminal node of the regression tree [27]. e accuracy of the GB model depends on hyperparameter values such as the number of trees m, the learning rate η, and the maximum number of splits (MaxNumSplits).…”

Section: Gradient Boosting Gradient Boosting (Gb) Algorithmmentioning

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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“…Having a reliable forecast of solar irradiance (SIr) is of great importance, due to its effect on the design of photovoltaic systems and measuring solar energy production [90,91]. Figure 1 shows solar radiation on a photovoltaic module installed on the earth.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

Moayedi¹,

Mosavi²

2021

Preprint

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Proper management of solar energy, as an effective renewable source, is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO) is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for non-linearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO (i.e., NPop, R_rate, Ps_rate, P_field, and N_field) are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

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Reliable solar irradiance prediction using ensemble learning-based models: A comparative study

Cited by 106 publications

References 39 publications

Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes

Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes

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

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

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