New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks

Marzouq, Manal; Bounoua, Zineb; Fadili, Hakim El; Mechaqrane, Abdellah; Zenkouar, Khalid; Lakhliai, Zakia

doi:10.1016/j.jclepro.2018.10.254

Cited by 41 publications

(10 citation statements)

References 43 publications

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“…In another research study, a KNN, empirical model, and ANN technique was used to predict solar energy radiations. As a result, K-NN achieved the R 2 values of 0.96, while another model was used to estimate the solar radiations having a R 2 value of 0.97 [22].…”

Section: Literature Reviewmentioning

confidence: 99%

A Time Series Forecasting of Global Horizontal Irradiance on Geographical Data of Najran Saudi Arabia

Alghamdi

2022

Energies

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Environment-friendly and renewable energy resources are the need of each developed and undeveloped country. Solar energy is one of them, thus accurate forecasting of it can be useful for electricity supply companies. This research focuses on analyzing the daily global solar radiation (GSR) data of Najran province located in Saudi Arabia and proposed a model for the prediction of global horizontal irradiance (GHI). The weather data is collected from Najran University. After inspecting the data, I we found the dependent and independent variables for calculating the GHI. A dataset model has been trained by creating tensor of variables belonging to air, wind, peak wind, relative humidity, and barometric pressure. Furthermore, six machine learning algorithms convolutional neural networks (CNN), K-nearest neighbors (KNN), support vector machines (SVM), logistic regression (LR), random forest classifier (RFC), and support vector classifier (SVC) techniques are used on dataset model to predict the GHI. The evaluation metrics determination coefficients (R2), root mean square error (RMSE), relative root mean square error (rRMSE), mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and T-statistic (t-stat) are used for the result verification of proposed models. Finally, the current work reports that all methods examined in this work may be utilized to accurately predict GHI; however, the SVC technique is the most suitable method amongst all techniques by claiming the precise results using the evaluation metrics.

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Section: Literature Reviewmentioning

confidence: 99%

A Time Series Forecasting of Global Horizontal Irradiance on Geographical Data of Najran Saudi Arabia

Alghamdi

2022

Energies

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“…Currently, several methods have been developed for predicting solar radiation using GIS and remote sensing techniques [125], artificial intelligence models [126], and empirical models. Marzouq et al [127] proposed an automatic identification of variables with the creation of the evolutionary ANN for forecasting solar radiation in Morocco, with good accuracy. The deep learning method from artificial neural networks (ANN) has been established in the study for solar radiation modeling.…”

Section: Estimating Solar Radiation Using Gismentioning

confidence: 99%

Energy Consumption and Spatial Assessment of Renewable Energy Penetration and Building Energy Efficiency in Malaysia: A Review

et al. 2021

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The development of sustainable energy systems is very important to addressing the economic, environmental, and social pressures of the energy sector. Globally, buildings consume up to 40% of the world’s total energy. By 2030, it is expected to increase to 50%. Therefore, the world is facing a great challenge to overcome these problems related to global energy production. Malaysia is one of the top consumers of primary energy in Asia. In 2018, primary energy consumption for Malaysia was 3.79 quadrillion btu at an average annual rate of 4.58%. In this paper, we have carried out a detailed literature review on several previous studies of energy consumption in the world, especially in Malaysia, and how geographical information system (GIS) methods have been used for the spatial assessment of energy efficiency. Indeed, strategies of energy efficiency are essential in energy policy that could be created using various approaches used for energy savings in buildings. The findings of this review reveal that, for estimating energy consumption, exploring renewable energy sources, and investigating solar radiation, several geographic information system techniques such as multiple criteria decision analysis (MCDA), machine learning (ML), and deep learning (DL) are mainly utilized. The result indicates that the fuzzy DS method can more reliably determine the optimal PV farm locations. The 3D models are also regarded as an effective tool for estimating solar radiation, since this method generates a 3D model exportable to software tools. In addition, GIS and 3D can contribute to several purposes, such as sunlight access to buildings in urban areas, city growth prediction models and analysis of the habitability of public places.

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“…In the meteorological field, many researchers have studied the use of single neural network (SNN) models for predicting global solar radiation, as seen in the literature. [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] However, ANN has been reproved for its nature, which lead to difficulties in understanding the linearity or quadratic dependency of the transfer equations. Furthermore, the computational cost, as well as the issue of overfitting, was found.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hourly Global Solar Radiation: Comparison of Neural Networks / Bootstrap Aggregating

Dahmani

Ammi

Hanini

et al. 2023

Kemija U Industriji

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This research work explores the use of single neural networks and bootstrap aggregated neural networks for predicting hourly global solar radiation. A database of 3606 data points were from the Renewable Energies Development Center, radiometric station 'Shems' of Bouzareah. The single neural networks and bootstrap aggregated neural networks were built together. The precision and durability of neural network models generated with an incomplete quantity of training datasets were improved using bootstrap aggregated neural networks. To produce numerous sets of training data points, the training data was re-sampled utilising bootstrap resampling by replacement. A neural network model was built for each of the data points. The individual neural network models were then combined to produce the bootstrap aggregated neural networks. The experimental and predicted values of global solar radiation were compared, and lower root mean squared errors (68.3968 and 62.4856 Wh m −2 ) were discovered during the testing phases for single neural networks and bootstrap aggregated neural networks, respectively. The results of these models show that the bootstrap aggregated neural networks model is more accurate and robust than single neural networks.

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New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks

Cited by 41 publications

References 43 publications

A Time Series Forecasting of Global Horizontal Irradiance on Geographical Data of Najran Saudi Arabia

A Time Series Forecasting of Global Horizontal Irradiance on Geographical Data of Najran Saudi Arabia

Energy Consumption and Spatial Assessment of Renewable Energy Penetration and Building Energy Efficiency in Malaysia: A Review

Prediction of Hourly Global Solar Radiation: Comparison of Neural Networks / Bootstrap Aggregating

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