Use of the artificial neural network and meteorological data for predicting daily global solar radiation in Djelfa, Algeria

Assas, Ouarda; Bouzgou, Hassen; Fetah, S.; Salmi, Mohamed; Boursas, A.

doi:10.1109/iccmrea.2014.6843807

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…Among these methods, some are based on linear models such as Linear Regression (LR), Auto-Regressive Moving Average (ARMA) and Auto-Regressive (AR) [1,2]. However, because of the nonlinear behavior of the solar radiation, researchers propose several nonlinear models based on wavelet-based methods, fuzzy models, Adaptive Neural Fuzzy Inference Systems (ANFIS) Random Forests (RF), k-Nearest Neighbors (kNN) and Artificial Neural Networks (ANN) [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

et al. 2018

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Abstract:The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don't satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.

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Section: Introductionmentioning

confidence: 99%

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

et al. 2018

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“…The study has shown that the performance of ANN models, geographical parameters, days of the year and sunlight are the most important factors for predicting daily global solar irradiation. Assas et al [22] have estimated the irradiation in Djelfa, Algeria, with data on irradiation, relative humidity, temperature, atmospheric pressure and wind speed provided by the Algerian meteorological station, with the ANN network model. Alluhaidah et al [23] using weather temperature, relative humidity, pressure, cloud cover, wind speed and direction and day information obtained between 2007 and 2010 in Riyadh, Saudi Arabia, they determined that cloud cover was the most effective parameter for predicting solar irradiation with the ANN model.…”

Section: Highlightsmentioning

confidence: 99%

Solar irradiation estimation with meteorological data using multi layer neural network approach

YÜZER

2023

Clean Energy Technol J

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The depletion of fossil fuels and the release of carbon dioxide into the atmosphere have increased the importance of alternative energy sources. Therefore, electricity generation is increasing using renewable energy sources. Solar energy has an important place among renewable energy sources. The reach of solar irradiation to the earth, which is an important parameter for solar power plants, depends on different climatic conditions. The efficiency of the solar power plant depends on the predictive accuracy of the solar irradiation. Accurate irradiation estimation improves the efficiency of the Photovoltaic (PV) plant, enabling accurate and efficient programming of the grid and improving power quality. In this study, simultaneous solar radiation values were predicted through a Multilayer Perceptron (MLP) model utilizing atmospheric pressure, relative humidity, ambient temperature, and wind speed parameters obtained from a station established for the measurement of meteorological data. Furthermore, the relationships between the input parameters employed in the prediction model and the output parameter, which is the solar radiation value, were investigated, along with their impact on the prediction accuracy. In the study using the error test method, solar irradiation values were estimated with high accuracy.

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“…Initially, the algorithm uses the non-linear gradient optimization method. Among the best known are the Quasi-Newton method and Levenberg-Marquardt which is used in our study [15,17,18].…”

Section: Figure 2 Mlp With One Hidden Layermentioning

confidence: 99%

Estimation of daily vertical solar irradiation by the use of meteorological data

Benziane

Ayad

Saoudi

et al. 2019

IJAPE

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Energy has been recognized as one of the most inputs for social and economic improvement and a clean energy is a challenge in the future. Therefore, renewable energy has become one of the most popular sources of energy. The solar energy, is one of this renewable energy, has the most propriety which is the durability. Due to its geographical location, Algeria has one of the world’s most important gas reserves, and has in addition one of the highest solar reservoirs in the world. Solar irradiation is an essential parameter for many applications such as the design and performance of renewable energy systems, but this is not always available, especially in remote locations. The prediction of solar irradiation values is often the only practical way to acquire these data. The objective of this work is to develop a model for the prediction of the Vertical Solar Irradiation (VSI) based on real meteorological data. This model is based on the Artificial Neural Networks (ANN).

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Use of the artificial neural network and meteorological data for predicting daily global solar radiation in Djelfa, Algeria

Cited by 13 publications

References 8 publications

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

Solar irradiation estimation with meteorological data using multi layer neural network approach

Estimation of daily vertical solar irradiation by the use of meteorological data

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