ANN-based modelling and prediction of daily global solar irradiation using commonly measured meteorological parameters

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

doi:10.1088/1755-1315/161/1/012017

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…However, the MAPE results for 2016 and 2017 in this study are classified under high accurate prediction, while the results for 2018 and 2019 are classified as good estimation according to Lewis classification [34]. The value of MAPE in this study also lower than similar study in Fez, Morocco, in which the best achieved MAPE is 21.77% [37].…”

Section: Resultscontrasting

confidence: 75%

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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Section: Resultscontrasting

confidence: 75%

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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“…Similar to neurons that are found inside a human brain, the ANN is made up of a number of neurons. The weight of these neurons is a fractional number that represents their connection to one another [123], [124]. These neurons are related to one another by this weight.…”

Section: A Artificial Neural Networkmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

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“…Physical models simulate PV systems employing complicated equations, but they are expensive to compute and can fall short of capturing the subtleties of actual circumstances, which leads to less precise forecasts. Similar short-term variations and quick changes in meteorological conditions, which have a significant effect on solar PV generation and lead to inaccurate predictions, are also difficult for statistical techniques based on historical data patterns and statistical algorithms to capture (Antonopoulos and Antonopoulos, 2024;Marzouq et al, 2018; A. . ML models, on the other hand, can enhance accuracy by combining historical trends, incorporating real-time data, and adapting to changing circumstances (Jathar et al, 2024).…”

Section: Application Of ML In Solar Energymentioning

confidence: 99%

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

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This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

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ANN-based modelling and prediction of daily global solar irradiation using commonly measured meteorological parameters

Cited by 8 publications

References 9 publications

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

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

Artificial intelligence applications in solar energy

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

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