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Yıl 2024, Cilt: 4 Sayı: 1, 36 - 52, 30.08.2024

Jamilu Ya'u Muhammad , Abubakar Abdulkarim Nafi’u Muhammad Saleh , Israel Ehile Nuraini Sunusi Ma’aji Audu Taofeek Olaniyi

https://doi.org/10.54569/aair.1483394

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Kaynakça

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Recent Progress on Applications of Artificial Intelligence for Sustainability of Solar Energy Technologies: An Extensive Review

Yıl 2024, Cilt: 4 Sayı: 1, 36 - 52, 30.08.2024

Jamilu Ya'u Muhammad , Abubakar Abdulkarim Nafi’u Muhammad Saleh , Israel Ehile Nuraini Sunusi Ma’aji Audu Taofeek Olaniyi

https://doi.org/10.54569/aair.1483394

Öz

Green energy sources are most promising energy sources in the globe, as they are non-pollutant sources. Solar energy sources are among green energy sources that are free and abundant in nature, yet solar energy sources have some shortcoming such as faults on the solar PV modules, improper maintenance and some climatic and environmental impacts. Artificial intelligences are employed to solve most of these shortcoming like prediction of the solar irradiance of the specific sites, parameters estimation on the solar PV modules, fault detection on the solar PV modules surfaces and forecasting of solar PV power output. This paper presents extensive review on application of artificial intelligences to solve problems related to solar energy systems from 2009 to 2024. It was found that from most of the literatures, artificial intelligent algorithms were more accurate and efficient than the conventional methods and it has an ability to solve complex and non-linear data. This work will help scholars to explore the relationship between solar energy technologies and artificial intelligences.

Anahtar Kelimeler

Artificial neutral network Genetic algorithms Machine learning Particle swarm optimization Solar irradiance

Kaynakça

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  • Qin, S.; Li, M.; Guanjun, L.; Jianzhong, Z.; Yongchuan, Z.; Pinan, R. “Short-Term Load Forecasting Based on Multi-Scale Ensemble Deep Learning Neural Network”, IEEE Access, 11 (2023) 111963-111975, https://doi.org/10.1109/ACCESS.2023.3322167
  • Ribeiro, R.; Fanzeres, B. “Identifying representative days of solar irradiance and wind speed in Brazil using machine learning techniques”, Energy and AI, (2023). https://doi.org/10.1016/j.egyai.2023.100320.
  • Sebastian, Z.; Dazhi, Y.; Tomas, L.; Pietro, E. C. “Site adaptation with machine learning for a Northern Europe gridded global solar irradiance product”, Energy and AI, (2023). https://doi.org/10.1016/j.egyai.2023.100331
  • Sheng, W.; Li, R.; Shi, L.; Lu, T. “Distributed photovoltaic short-term power forecasting using hybrid competitive particle swarm optimization support vector machines based on spatial correlation analysis”, IET Renew. Power Gener., (2023) 1–14. https://doi.org/10.1049/rpg2.12860
  • Shihan, L.; Chun, Y.; Yuan, G. “Short-term photovoltaic power prediction model based on quadratic frequency domain decomposition algorithm for neural networks”, In Proc. SPIE 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 1278811 (25 September 2023); https://doi.org/10.1117/12.3004418
  • Wen, S.-B.; Bhaskar, A. “The Shockley–Queisser Efficiency Limit of Solar Thermophotovoltaic (STPV) Cells using Different Photovoltaic Cells and a Radiation Shield Considering the Étendue of Solar Radiation”, Energies, 16 (2023) 7085. https://doi.org/10.3390/en16207085
  • Zhao, H.; Zhu, D.; Yang, Y.; Li, Q.; Zhang, E. “Study on photovoltaic power forecasting model based on peak sunshine hours and sunshine duration”, Energy Sci. Eng., (2023) 1‐11. https://doi.org/10.1002/ese3.1598
  • Zhu, J.; Zhao, Z.; Zheng, X.; An, Z.; Guo, Q.; Li, Z.; Sun, J.; Guo, Y. “Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer”; Energies, 16 (2023) 7610. https://doi.org/10.3390/en16227610
  • Li, G.; Ding, C.; Zhao, N.; Wei, J.; Guo, Y.; Meng, C.; Huang, K.; Zhu, R. “Research on a novel photovoltaic power forecasting model based on parallel long and short-term time series network”, Energy, 293 (2024) 130621. https://doi.org/10.1016/j.energy.2024.130621
  • Liu, J.; Li, T. “Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model”, Energy, 293 (2024) 130468. https://doi.org/10.1016/j.energy.2024.130468
  • Murugan, R.; Arunachalam, S.; Hussein, M. R.; Seyedali, M. “Estimation of photovoltaic models using an enhanced Henry gas solubility optimization algorithm with first-order adaptive damping Berndt-Hall-Hall-Hausman method”, Energy Conversion and Management, 299 (2024) 117831, https://doi.org/10.1016/j.enconman.2023.117831
  • Nicoletti, F.; Bevilacqua, P. “Hourly Photovoltaic Production Prediction using Numerical Weather Data and Neural Networks for Solar Energy Decision Support”, Energies, 17 (2024) 466. https://doi.org/10.3390/en17020466
  • Wang, J.; Si, Y.; Zhu, Y.; Zhang, K.; Yin, S.; Liu, B. “Cyberattack detection for electricity theft in smart grids via stacking ensemble GRU optimization algorithm using federated learning framework”, Electrical Power and Energy Systems, 157 (2024) 109848; https://doi.org/10.1016/j.ijepes.2024.109848
Toplam 102 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka (Diğer)
Bölüm Derlemeler
Yazarlar

Jamilu Ya'u Muhammad 0000-0002-7627-672X

Abubakar Abdulkarim Bu kişi benim 0009-0003-5256-4916

Nafi’u Muhammad Saleh 0009-0001-2414-1655

Israel Ehile Bu kişi benim 0009-0004-5243-3550

Nuraini Sunusi Ma’aji Bu kişi benim 0000-0003-4384-6826

Audu Taofeek Olaniyi Bu kişi benim 0009-0005-1648-8420

Yayımlanma Tarihi 30 Ağustos 2024
Gönderilme Tarihi 16 Mayıs 2024
Kabul Tarihi 30 Ağustos 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 4 Sayı: 1

Kaynak Göster

IEEE J. Y. Muhammad, A. Abdulkarim, N. M. Saleh, I. Ehile, N. S. Ma’aji, ve A. T. Olaniyi, “Recent Progress on Applications of Artificial Intelligence for Sustainability of Solar Energy Technologies: An Extensive Review”, Adv. Artif. Intell. Res., c. 4, sy. 1, ss. 36–52, 2024, doi: 10.54569/aair.1483394.
 Kapak Resmi İndir

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