Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review

Nawab, Faisal; Hamid, Ag Sufiyan Abd; Ibrahim, Adnan; Sopian, Kamaruzzaman; Fazlizan, Ahmad; Faizal, Mohd

doi:10.1016/j.heliyon.2023.e17038

Cited by 8 publications

(2 citation statements)

References 94 publications

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“…A large amount of attention has been drawn to the use of machine learning (ML) methods in solar prediction because these approaches have the potential to increase the accuracy and reliability of solar radiation forecasting [312], [313]. Gaussian process regression (GPR) and wavelet Three years of data was used for model training and a fourth year of data was used for comparison…”

Section: Table I the Following Is A Summary Of The Application Of Sof...mentioning

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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Section: Table I the Following Is A Summary Of The Application Of Sof...mentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…Statistic metrics [29] Diffuse solar radiation RRMSE, MAE [30] Humidity, wind speed, temperature & pressure RMSE, MAE, MSE [31] Surface meteorological measurements MAbE, RMSE [32] Minimum and maximum values of temperature, humidity, wind speed and solar irradiation MSE, MAE, RMSE and MAPE [33] Wind speed, sun height, ambient temperature MAE, RMSE, NMBE [34] Wind speed, dew-point, relative humidity, wind RMSE, NMBE, R², CV(RMSE) direction, outdoor airdry bulb temperature [35] Hourly solar radiation R² and RMSE [36] Clear…”

Section: Reference Input Parametermentioning

confidence: 99%

Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin

Marques,

Teixeira,

De Almeida

et al. 2024

IEEE Access

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Deep learning has grown among the prediction tools used within renewable energy options. Solar energy belongs to the options with the lowest atmosphere impact after considering their limitations. In the last five years, Brazil has seen the expansion of wind and solar options almost all over the country, and to preserve the Amazon rainforest, the use of solar energy has helped large and small cities towards a greener future. The novelty of this research covers the use of Deep Learning with data from twelve cities in the state of Amazonas to forecast solar irradiation (W.h/m 2 ) within 30 days. The data input came from ground stations, as much as possible, and NASA satellite models, with a daily time aggregation. The types of neural networks considered are Long Short-Term Memory (LSTM), a Multi-Layer Perceptron (MLP), and an LSTM Gated Recurrent Unit (GRU). Among the metrics used to check the algorithm's performance, the Mean Absolute Percentage Error (MAPE) indicates that the values of this research are coherent with other scenarios to forecast solar energy; the boundary conditions were not the same, however. The lowest MAPE was observed in the city of Labrea with the LSTM GRU.

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