Machine learning-based improvement of empiric models for an accurate estimating process of global solar radiation

Demircan, Cihan; Bayrakçı, Hilmi Cenk; Keçebaş, Ali

doi:10.1016/j.seta.2019.100574

Cited by 15 publications

(10 citation statements)

References 39 publications

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“…The numerical results showed that support vector machines with bat algorithms (SVM-BAT) models performed the best in terms of the prediction accuracy and the rate of convergence. Demircan et al [145] designed an empirical regression model with the artificial bee colony (ABC) algorithms to predict global solar radiation. The forecasting model depended on durations and angles of sunlight to make a forecast.…”

Section: Parameter Selection Of Machine-learning Models In Renewable-...mentioning

confidence: 99%

A Survey of Machine Learning Models in Renewable Energy Predictions

et al. 2020

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The use of renewable energy to reduce the effects of climate change and global warming has become an increasing trend. In order to improve the prediction ability of renewable energy, various prediction techniques have been developed. The aims of this review are illustrated as follows. First, this survey attempts to provide a review and analysis of machine-learning models in renewable-energy predictions. Secondly, this study depicts procedures, including data pre-processing techniques, parameter selection algorithms, and prediction performance measurements, used in machine-learning models for renewable-energy predictions. Thirdly, the analysis of sources of renewable energy, values of the mean absolute percentage error, and values of the coefficient of determination were conducted. Finally, some possible potential opportunities for future work were provided at end of this survey.

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Section: Parameter Selection Of Machine-learning Models In Renewable-...mentioning

confidence: 99%

A Survey of Machine Learning Models in Renewable Energy Predictions

et al. 2020

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“…It was reported that the proposed algorithm has a lower mean absolute percentage error (MAPE) and outperforms the classical SVR models by 32.15-39.69% for the selected locations of the study. Demircan et al [11] improved the performance of the empirical Angstrom-Prescott model for solar radiation estimation using the Artificial Bee Colony (ABC) algorithm for the city of Mugla, Turkey. Both annual and semi-annual models were developed, and the performance was enhanced using the computational algorithm.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

El-kenawy¹,

Ibrahim²,

Bailek³

et al. 2022

Computers, Materials &Amp; Continua

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In order to achieve a highly accurate estimation of solar energy resource potential, a novel hybrid ensemble-learning approach, hybridizing Advanced Squirrel-Search Optimization Algorithm (ASSOA) and support vector regression, is utilized to estimate the hourly tilted solar irradiation for selected arid regions in Algeria. Long-term measured meteorological data, including mean-air temperature, relative humidity, wind speed, alongside global horizontal irradiation and extra-terrestrial horizontal irradiance, were obtained for the two cities of Tamanrasset-and-Adrar for two years. Five computational algorithms were considered and analyzed for the suitability of estimation. Further two new algorithms, namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization approach. The accuracy of the developed models was analyzed in terms of five statistical error metrics, as well as the Wilcoxon rank-sum and ANOVA test. Among the previously selected algorithms, K Neighbors Regressor and support vector regression exhibited good performances. However, the newly proposed ensemble algorithms exhibited even better performance. The proposed model showed relative root mean square errors lower than 1.448% and correlation coefficients higher than 0.999. This was further verified by benchmarking the new ensemble against several popular swarm intelligence algorithms. It is concluded that the proposed algorithms are far superior to the commonly adopted ones.

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“…Solar photovoltaic (PV) modules of various sizes have been commercialized due to their potential long term economic and environmental benefits [1][2][3][4]. The prospects of PV are enhanced by continuous price reduction in the modules and inverter.…”

Section: Introductionmentioning

confidence: 99%

“…The prospects of PV are enhanced by continuous price reduction in the modules and inverter. However, like most sustainable energy sources, its availability is intermittent due to varying weather conditions [2,[5][6][7][8][9][10][11][12][13]. It is established that PV power depends on various complex weather conditions like temperature, radiation, wind speed, dust and humidity.…”

Section: Introductionmentioning

confidence: 99%

An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data

et al. 2021

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In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10−7 to 3.19 × 10−10. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.

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Machine learning-based improvement of empiric models for an accurate estimating process of global solar radiation

Cited by 15 publications

References 39 publications

A Survey of Machine Learning Models in Renewable Energy Predictions

A Survey of Machine Learning Models in Renewable Energy Predictions

Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data

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