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Sky image-based solar irradiance prediction methodologies using artificial neural networks

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  • Kamadinata, Jane Oktavia
  • Ken, Tan Lit
  • Suwa, Tohru

Abstract

In order to decelerate global warming, it is important to promote renewable energy technologies. Solar energy, which is one of the most promising renewable energy sources, can be converted into electricity by using photovoltaic power generation systems. Whether the photovoltaic power generation systems are connected to an electrical grid or not, predicting near-future global solar radiation is useful to balance electricity supply and demand.

Suggested Citation

  • Kamadinata, Jane Oktavia & Ken, Tan Lit & Suwa, Tohru, 2019. "Sky image-based solar irradiance prediction methodologies using artificial neural networks," Renewable Energy, Elsevier, vol. 134(C), pages 837-845.
    • Handle: RePEc:eee:renene:v:134:y:2019:i:c:p:837-845
    DOI: 10.1016/j.renene.2018.11.056
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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances," Renewable Energy, Elsevier, vol. 80(C), pages 770-782.
    3. Alonso, J. & Batlles, F.J. & López, G. & Ternero, A., 2014. "Sky camera imagery processing based on a sky classification using radiometric data," Energy, Elsevier, vol. 68(C), pages 599-608.
    4. Barbieri, Florian & Rajakaruna, Sumedha & Ghosh, Arindam, 2017. "Very short-term photovoltaic power forecasting with cloud modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 242-263.
    5. Tzoumanikas, P. & Nikitidou, E. & Bais, A.F. & Kazantzidis, A., 2016. "The effect of clouds on surface solar irradiance, based on data from an all-sky imaging system," Renewable Energy, Elsevier, vol. 95(C), pages 314-322.
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    Cited by:

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    2. Liu, Jingxuan & Zang, Haixiang & Ding, Tao & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2023. "Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting," Renewable Energy, Elsevier, vol. 209(C), pages 619-631.
    3. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2022. "A network of sky imagers for spatial solar irradiance assessment," Renewable Energy, Elsevier, vol. 187(C), pages 1009-1019.
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    5. Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting," Applied Energy, Elsevier, vol. 342(C).
    6. Wen-Chi Kuo & Chiun-Hsun Chen & Sih-Yu Chen & Chi-Chuan Wang, 2022. "Deep Learning Neural Networks for Short-Term PV Power Forecasting via Sky Image Method," Energies, MDPI, vol. 15(13), pages 1-17, June.
    7. Zheng, Yuanzhou & Shadloo, Mostafa Safdari & Nasiri, Hossein & Maleki, Akbar & Karimipour, Arash & Tlili, Iskander, 2020. "Prediction of viscosity of biodiesel blends using various artificial model and comparison with empirical correlations," Renewable Energy, Elsevier, vol. 153(C), pages 1296-1306.
    8. Zhiying Lu & Wenpeng Chen & Qin Yan & Xin Li & Bing Nie, 2023. "Photovoltaic Power Forecasting Approach Based on Ground-Based Cloud Images in Hazy Weather," Sustainability, MDPI, vol. 15(23), pages 1-19, November.
    9. Xie, Qiyue & Ma, Lin & Liu, Yao & Fu, Qiang & Shen, Zhongli & Wang, Xiaoli, 2023. "An improved SSA-BiLSTM-based short-term irradiance prediction model via sky images feature extraction," Renewable Energy, Elsevier, vol. 219(P2).
    10. Marzouq, Manal & El Fadili, Hakim & Zenkouar, Khalid & Lakhliai, Zakia & Amouzg, Mohammed, 2020. "Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data," Renewable Energy, Elsevier, vol. 157(C), pages 214-231.
    11. Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.
    12. Zhang, Liwenbo & Wilson, Robin & Sumner, Mark & Wu, Yupeng, 2023. "Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer mechanism approach," Renewable Energy, Elsevier, vol. 216(C).
    13. Sebastián Vázquez-Ramírez & Miguel Torres-Ruiz & Rolando Quintero & Kwok Tai Chui & Carlos Guzmán Sánchez-Mejorada, 2023. "An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model," Mathematics, MDPI, vol. 11(14), pages 1-26, July.
    14. Ajith, Meenu & Martínez-Ramón, Manel, 2021. "Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data," Applied Energy, Elsevier, vol. 294(C).
    15. Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).

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