Prediction of daily global horizontal solar irradiation using artificial neural networks and commonly measured meteorological parameters

Bounoua, Zineb; Mechaqrane, Abdellah

doi:10.1063/1.5084997

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Conversely, employing a small number of neurons fails to capture the intricate relationships between previous and subsequent values. Consequently, a trial-and-error approach is commonly employed to identify the optimal number of hidden neurons within the hidden layer [31,55,56].…”

Section: Ann-based Modelsmentioning

confidence: 99%

Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods

Ali,

Elsayed,

Elkabani

et al. 2023

Energies

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Obtaining precise solar radiation data is the first stage in determining the availability of solar energy. It is also regarded as one of the major inputs for a variety of solar applications. Due to the scarcity of solar radiation measurement data for many locations throughout the world, many solar radiation models are utilized to predict global solar radiation. Indeed, the most widely used AI technique is artificial neural networks (ANNs). Hitherto, while ANNs have been utilized in various studies to estimate global solar radiation (GSR), limited attention has been given to the architecture of ANN. Thus, this study aimed to: first, optimize the design of one of the faster and most used machine-learning (ML) algorithms, the ANN, to forecast GSR more accurately while saving computation power; second, optimize the number of neurons in the hidden layer to obtain the most significant ANN model for accurate GSR estimation, since it is still lacking; in addition to investigating the impact of varying the number of neurons in the hidden layer on the proficiency of the ANN-based model to predict GSR with high accuracy; and, finally, conduct a comparative study between the ANN and empirical techniques for estimating GSR. The results showed that the best ANN model and the empirical model provided an excellent estimation for the GSR, with a Coefficient of Determination R2 greater than 0.98%. Additionally, ANN architectures with a smaller number of neurons in the single hidden layer (1–3 neurons) provided the best performance, with R2 > 0.98%. Furthermore, the performance of the developed ANN models remained approximately stable and excellent when the number of hidden layer’s neurons was less than ten neurons (R2 > 0.97%), as their performance was very close to each other. However, the ANN models experienced performance instability when the number of hidden layer’s neurons exceeded nine neurons. Furthermore, the performance comparison between the best ANN-based model and the empirical one revealed that both models performed well (R2 > 0.98%). Moreover, while the relative error for the best ANN model slightly exceeded the range, ±10% in November and December, it remained within the range for the empirical model even in the winter months. Additionally, the obtained results of the best ANN model in this work were compared with the recent related work. While it had a good RMSE value of 0.8361 MJ/m2 day−1 within the ranges of previous work, its correlation coefficient (r) was the best one. Therefore, the developed models in this study can be utilized for accurate GSR forecasting. The accurate and efficient estimation of global solar radiation using both models can be valuable in designing and performance evaluation for different solar applications.

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Section: Ann-based Modelsmentioning

confidence: 99%

Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods

Ali,

Elsayed,

Elkabani

et al. 2023

Energies

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“…2019'daki pandemi öncesi seviyesinden neredeyse %50 daha yüksektir. Güneş radyasyonu, güneş enerjisi araştırmaları için önemli bir parametredir ancak meteoroloji istasyonlarında güneş radyasyonu ölçüm ekipmanlarının bulunmaması nedeniyle sahaların çoğu için mevcut değildir (Bounoua, Z., and Mechaqrane, A., 2018). Özelde güneş radyasyonunun ve genel olarak zaman serilerinin tahmini, son araştırmaların ana konusu olarak önemli ilgi görmüştür.…”

Section: Introductionunclassified

Design of a Hybrid Method Exploiting Different Insolation States for Solar Radiation Forecasting

MAHAM

2022

Afyon Kocatepe University Journal of Sciences and Engineering

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The constant expansion of solar energy has made the accurate forecasting of radiation an important issue. An accurate prediction of solar energy production is crucial for the effective integration of photovoltaic (PV) and wind generators in smart grids. The intermittent nature of solar energy poses many challenges to renewable energy system operators in terms of operational planning and scheduling. For this reason, forecasting solar radiation by means of the hybrid methods is becoming widespread. In this paper, a hybrid method for predicting solar radiation is proposed, wherein the prediction model is determined based on the clearness index. The study used two-year solar radiation data of the province of Mardin obtained from the Turkish State Meteorological Service (TSMS). As predictors, ANN, NARX networks, and Ridge regression methods were used, and the training data were modeled with all three approaches in the first stage of the study. The clearness index was determined into three ranges; slightly cloudy, cloudy, and mostly cloudy. The training data were modeled with three methods used as estimators, and the success of each method was examined in each defined clearness index range. As a result, in the hybrid prediction algorithm, the clearness index is first estimated using artificial neural networks, and then the future solar radiation value is predicted by using the most successful model within the predicted clearness index range. Experimental results show that more successful predictions are made with the proposed hybrid method than when models are used individually.

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New categorized machine learning models for daily solar irradiation estimation in southern Morocco's, Zagora city

Bounoua,

Chahidi,

Mechaqrane

2024

e-Prime - Advances in Electrical Engineering, Electronics and E

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Prediction of daily global horizontal solar irradiation using artificial neural networks and commonly measured meteorological parameters

Cited by 5 publications

References 15 publications

Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods

Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods

Design of a Hybrid Method Exploiting Different Insolation States for Solar Radiation Forecasting

New categorized machine learning models for daily solar irradiation estimation in southern Morocco's, Zagora city

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