Estimation of the Global Solar Radiation with the Artificial Neural Networks for the City of Sivas

Gürlek, Cahit; Şahin, Mustafa

doi:10.26701/ems.359681

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Most of the studies to determine the solar radiation potential of a certain location were focused on the determination of total radiation falling on a horizontal surface using measured data [4][5][6][7]. Several solar radiation calculation models are performed in the literature by using Artificial Neural Networks [8,9] and Machine Learning [10,11]. The studies were carried out optimization of tilt angles and maximization of solar radiation falling on the tilted surface for the different locations are contributed to Türkiye in this field [12][13][14][15][16].…”

Section: Literature Reviews and Related Workmentioning

confidence: 99%

Maximization of Solar Radiation for Fixed and Tracking Surfaces in Antalya Province of Türkiye

Akgül

Alisinanoğlu

Hasoğlu

et al. 2023

Teh. glas. (Online)

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Solar energy has gained increasing importance in today's world and become a viable primary energy source in the recent decade. Solar radiation obtained by the solar surface is highly affected by its orientation, azimuth, and tilt angles. Therefore, in this study, the performances of the fixed-axis system, one-axis, and two-axis solar tracking systems are investigated to enable maximal solar radiation in solar systems to be installed in Antalya by using climatic and latitude data provided by NASA. Furthermore, the optimal tilt angles are determined by examining the values of angles for which the total solar radiation falling on the tilted surface is maximal. The case study and measurement data investigations are conducted for the four districts of Antalya. The obtained radiation values throughout the year for one-axis and two-axis solar tracking systems are compared to an annual fixed system for evaluating the existing solar potential in Antalya province. Besides all these, solar system cost analyses including the average payback period for residential, commercial, and large-scale solar systems based on LCOE are investigated. The proposed methodology can be implemented for performance and cost analysis of the solar potential in a certain location of Türkiye and extended to any place in the world.

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Section: Literature Reviews and Related Workmentioning

confidence: 99%

Maximization of Solar Radiation for Fixed and Tracking Surfaces in Antalya Province of Türkiye

Akgül

Alisinanoğlu

Hasoğlu

et al. 2023

Teh. glas. (Online)

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“…These models are required to display the hard correlation between weather conditions and solar radiation, which results in exact prediction compared to the conventional models, similar linear regression or fuzzy logic models (Wang et al, 2018). Different investigations are interested in studying the performance of the artificial neural network approach, as the investigation conducted by Gueymard et al (Gurlek & Sahin, 2018). The researchers confirmed the importance of employing efficient models for accurately predicting solar radiation in the globe.…”

Section: Introductionmentioning

confidence: 97%

Artificial Neural Networks for Solar Radiation Prediction: Case Study, Al-Qadisiyah, Iraq

Al-rubaye,

M. Al-Khuzaie

2024

Jou. Eng. Res.

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For solar energy to develop a clean, renewable alternative to fossil fuels, it is important to be able to correctly predict surface longwave radiation. To improve cost-efficiency and accuracy in surface longwave radiation (SLR) predictions, forecast systems are increasingly utilizing artificial neural networks (ANNs). This study uses two different procedures for predicting solar radiation in great detail. The first model uses weather statistics from a station in Al-Qadisiyah, Iraq. The second model, on the other hand, uses daily statistics from 2022 from NASA's Prediction of Worldwide Energy dataset for the same site. The scaled conjugate gradient technique was used in both models. The goal is to find the best mix of meteorological factors that can be used with an ANN model to achieve accurate predictions. Based on the findings of this study, temperature, relative humidity, and Rainfall all seem to have a big effect on SLR. On the other hand, windy weather doesn't have much of an effect on SLR. ANN models also did very well when trained with data from NASA's Prediction of Worldwide Energy, with an R2-value of 0.823 and an RMSE value of 0.0106. The results show that this mix does better than other models in terms of performance score.

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“…However, the data of measured solar radiation is limitedly available. Furthermore, some design processes demand the prediction of the future global solar radiationTherefore, the prediction or estimation of the global solar radiation is necessary [14], [15]. Numerous approaches have been proposed to estimate the amount of solar radiation on horizontal plane.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

Kurniawan

Harumwidiah

2021

IJEECS

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The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.

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Estimation of the Global Solar Radiation with the Artificial Neural Networks for the City of Sivas

Cited by 6 publications

References 26 publications

Maximization of Solar Radiation for Fixed and Tracking Surfaces in Antalya Province of Türkiye

Maximization of Solar Radiation for Fixed and Tracking Surfaces in Antalya Province of Türkiye

Artificial Neural Networks for Solar Radiation Prediction: Case Study, Al-Qadisiyah, Iraq

An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

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