A review of solar radiation prediction using artificial neural networks

Marzouq, Manal; Fadili, Hakim El; Lakhliai, Zakia; Zenkouar, Khalid

doi:10.1109/wits.2017.7934658

Cited by 19 publications

(13 citation statements)

References 42 publications

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“…where Gmeas is the measured value of the solar radiation and Gpred is the estimated value of the solar radiation. R 2 is selected as it is one of the most frequently used parameters to measure the accuracy of solar radiation prediction [9]. Therefore, by calculating the R 2 , the accuracy of the proposed method could easily be compared with the accuracy of the methods from previous studies.…”

Section: Resultsmentioning

confidence: 99%

“…Various methods have been proposed to estimate either the daily average or hourly solar radiation. The artificial neural network (ANN) method for estimation is one of the most popular methods for the daily average prediction due to its high accuracy [9,10]. While a wider range of methods has been used to estimate hourly solar radiation, ANN-based methods have also been applied with accurate results.…”

Section: Introductionmentioning

confidence: 99%

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Two-step artificial neural network to estimate the solar radiation at Java Island

Kurniawan

Shintaku

2021

IJECE

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<span>The availability of information about solar radiation characteristics, particularly solar radiation predictions, is important for efficiently designing solar energy systems. Solar radiation information is not available in Indonesia because official measurements have not been conducted by the Indonesian Meteorological, Climatology, and Geophysical Agency (BMKG). In this study, a new two-step artificial neural network (ANN) is proposed to estimate both the daily average and hourly solar radiation at Java Island, Indonesia. The input parameters for the daily average solar radiation estimation are the location and time required, along with five selected monthly meteorological parameters that BMKG predicts for the subsequent month. The selected meteorological parameters are temperatures, relative humidity, and precipitation. The estimated daily average solar radiation is then used as the input parameter of the hourly solar radiation estimation along with the local time and location. The ANN training was conducted using two years of data, 2018 and 2019, from Surabaya and Jakarta, while the validation was performed in the same cities for January through July 2020. The accuracy of the proposed method is comparable to previous studies with an average R2 of 98.70% for the daily average solar radiation estimate and 97.44% for the hourly solar radiation estimate.</span>

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-step artificial neural network to estimate the solar radiation at Java Island

Kurniawan

Shintaku

2021

IJECE

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“…This can be achieved by utilizing artificial neural networks (ANN) because of their ability to learn and train the system model. Artificial neural networks make a computational model to simulate nonlinear, complex, and time-varying systems [7].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of Solar Irradiance Based on Artificial Neural Networks

et al. 2019

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Prediction of solar irradiance plays an essential role in many energy systems. The objective of this paper is to present a low-cost solar irradiance meter based on artificial neural networks (ANN). A photovoltaic (PV) mathematical model of 50 watts and 36 cells was used to extract the short-circuit current and the open-circuit voltage of the PV module. The obtained data was used to train the ANN to predict solar irradiance for horizontal surfaces. The strategy was to measure the open-circuit voltage and the short-circuit current of the PV module and then feed it to the ANN as inputs to get the irradiance. The experimental and simulation results showed that the proposed method could be utilized to achieve the value of solar irradiance with acceptable approximation. As a result, this method presents a low-cost instrument that can be used instead of an expensive pyranometer.

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“…Recent methods employ machine learning algorithm in the form of artificial neural network (ANN) [18]- [20], gradient boosting tree (GBT) [21], genetic programming (GP) [22], [23], and support vector machine [24], [25] among others. In comparison with other estimation method,multi-layer perceptron ANN is one of the most popular since it solves high non-linearity, complex, and time-varying problems [26] and it has also been proven to have higher accuracy [27], [28].…”

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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A review of solar radiation prediction using artificial neural networks

Cited by 19 publications

References 42 publications

Two-step artificial neural network to estimate the solar radiation at Java Island

Two-step artificial neural network to estimate the solar radiation at Java Island

Prediction of Solar Irradiance Based on Artificial Neural Networks

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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