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Computers and Computing

A Survey of Machine Learning Applications in Renewable Energy Sources

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REFERENCES

  • M. S. Ibrahim, W. Dong, and Q. Yang, “Machine learning driven smart electric power systems: Current trends and new perspectives,” Appl. Energy, Vol. 272, no. February, pp. 115237, 2020. DOI: 10.1016/j.apenergy.2020.115237.
  • J. P. Lai, Y. M. Chang, C. H. Chen, and P. F. Pai, “A survey of machine learning models in renewable energy predictions,” Appl. Sci., Vol. 10, no. 17, 2020. DOI: 10.3390/app10175975.
  • H. Wang, Z. Lei, X. Zhang, B. Zhou, and J. Peng, “A review of deep learning for renewable energy forecasting,” Energy Convers. Manag., Vol. 198, no. April, pp. 111799, 2019. DOI: 10.1016/j.enconman.2019.111799.
  • A. G. Olabi, “Renewable energy and energy storage systems,” Energy, 2017. DOI: 10.1016/j.energy.2017.07.054.
  • S. Al-Yahyai, Y. Charabi, and A. Gastli, “Review of the use of numerical weather prediction (NWP) models for wind energy assessment,” Renew. Sustain. Energy Rev., Vol. 14, no. 9, pp. 3192–8, 2010. DOI: 10.1016/j.rser.2010.07.001.
  • J. Hu, J. Heng, J. Tang, and M. Guo, “Research and application of a hybrid model based on meta learning strategy for wind power deterministic and probabilistic forecasting,” Energy Convers. Manag., Vol. 173, no. April, pp. 197–209, 2018. DOI: 10.1016/j.enconman.2018.07.052.
  • A. Zendehboudi, M. A. Baseer, and R. Saidur, “Application of support vector machine models for forecasting solar and wind energy resources: A review,” J. Clean. Prod, Vol. 199, pp. 272–85, 2018. DOI: 10.1016/j.jclepro.2018.07.164.
  • K. Tharani, N. Kumar, V. Srivastava, S. Mishra, and M. Pratyush Jayachandran, “Machine learning models for renewable energy forecasting,” J. Stat. Manag. Syst., Vol. 23, no. 1, pp. 171–80, 2020. DOI: 10.1080/09720510.2020.1721636.
  • G. Priya, and S. Rhythm, “PV power forecasting based on data-driven models: A review,” Int. J. Sustain. Eng., Vol. 14, no. 6, pp. 1733–1755, 2021. DOI: 10.1080/19397038.2021.1986590.
  • K. Mahmud, S. Azam, A. Karim, S. Zobaed, B. Shanmugam, and D. Mathur, “Machine learning based PV power generation forecasting in Alice Springs,” IEEE. Access., Vol. 9, pp. 46117–28, 2021. DOI: 10.1109/ACCESS.2021.3066494.
  • S. Belaid, A. Mellit, H. Boualit, and M. Zaiani, “Hourly global solar forecasting models based on a supervised machine learning algorithm and time series principle,” Int. J. Ambient Energy, 1–25, 2020. DOI: 10.1080/01430750.2020.1718754.
  • O. Arif, Y. Alper, and B. Mehmet, “Deep learning approach for one-hour ahead forecasting of energy production in a solar-PV plant,” Energy Sources, Part A Recover. Util. Environ. Eff., 2021. DOI: 10.1080/15567036.2021.1924316.
  • O. Bamisile, A. Oluwasanmi, S. Obiora, E. Osei-Mensah, G. Asoronye, and Q. Huang, “Application of deep learning for solar irradiance and solar photovoltaic multi-parameter forecast,” Energy Sources, Part A Recover. Util. Environ. Eff., 1–21, 2020. DOI: 10.1080/15567036.2020.1801903.
  • C. Yildiz, and H. Acikgoz, “A kernel extreme learning machine-based neural network to forecast very short-term power output of an on-grid photovoltaic power plant,” Energy Sources, Part A Recover. Util. Environ. Eff., 1–18, 2020. DOI: 10.1080/15567036.2020.1801899.
  • W. Bendali, I. Saber, M. Boussetta, Y. Mourad, B. Bourachdi, and B. Bossoufi. “Deep learning for very short term solar irradiation forecasting,” in 2020 5th Int. Conf. Renew. Energies Dev. Countries, REDEC, 2020, vol. 5, 2020. DOI: 10.1109/REDEC49234.2020.9163897.
  • L. C. Parra Raffán, A. Romero, and M. Martinez, “Solar energy production forecasting through artificial neuronal networks, considering the Föhn, north and south winds in San Juan, Argentina,” J. Eng., Vol. 2019, no. 18, pp. 4824–9, 2019. DOI: 10.1049/joe.2018.9368.
  • M. Z. Hassan, M. E. K. Ali, A. B. M. S. Ali, and J. Kumar. “Forecasting day-ahead solar radiation using machine learning approach,” in Proc. – 2017 4th Asia-Pacific World Congr. Comput. Sci. Eng. APWC CSE, 2017, pp. 252–8, 2018. DOI: 10.1109/APWConCSE.2017.00050.
  • H. Zang, L. Cheng, T. Ding, K. W. Cheung, Z. Wei, and G. Sun, “Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning,” Int. J. Electr. Power Energy Syst., Vol. 118, no. February 2019, pp. 105790, 2020. DOI: 10.1016/j.ijepes.2019.105790.
  • Ü Ağbulut, A. E. Gürel, and Y. Biçen, “Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison,” Renew. Sustain. Energy Rev., Vol. 135, no. August 2020, 2021. DOI: 10.1016/j.rser.2020.110114.
  • K. Benhmed, et al. “PV power prediction in Qatar based on machine learning approach,” in Proc. 6th Int. Renew. Sustain. Energy Conf. IRSEC, pp. 1–4, 2018. DOI: 10.1109/IRSEC.2018.8702880.
  • T. Burianek, and S. Misak, “Solar irradiance forecasting model based on extreme learning machine,” Int. Conf. Environ. Electr. Eng., 0–4, 2016. DOI: 10.1109/EEEIC.2016.7555445.
  • Ü Ağbulut, A. E. Gürel, A. Ergün, and İ Ceylan, “Performance assessment of a V-trough photovoltaic system and prediction of power output with different machine learning algorithms,” J. Clean. Prod., Vol. 268, 2020. DOI: 10.1016/j.jclepro.2020.122269.
  • İ Üstün, F. Üneş, İ Mert, and C. Karakuş, “A comparative study of estimating solar radiation using machine learning approaches: DL, SMGRT, and ANFIS,” Energy Sources, Part A Recover. Util. Environ. Eff., 2020. DOI: 10.1080/15567036.2020.1781301.
  • B. K. Puah, et al., “A regression unsupervised incremental learning algorithm for solar irradiance prediction,” Renew. Energy, Vol. 164, pp. 908–25, 2021. DOI: 10.1016/j.renene.2020.09.080.
  • M. Afrasiabi, M. Mohammadi, M. Rastegar, and S. Afrasiabi, “Deep learning architecture for direct probability density prediction of small-scale solar generation,” IET Gener. Transm. Distrib., Vol. 14, no. 11, pp. 2017–25, 2020. DOI: 10.1049/iet-gtd.2019.1289.
  • J. Wang, L. Guo, C. Zhang, L. Song, J. Duan, and L. Duan, “Thermal power forecasting of solar power tower system by combining mechanism modeling and deep learning method,” Energy, Vol. 208, pp. 118403, 2020. DOI: 10.1016/j.energy.2020.118403.
  • M. Alizamir, S. Kim, O. Kisi, and M. Zounemat-Kermani, “A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions,” Energy, Vol. 197, pp. 117239, 2020. DOI: 10.1016/j.energy.2020.117239.
  • C. Koo, W. Li, S. H. Cha, and S. Zhang, “A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques,” Renew. Energy, Vol. 133, pp. 575–92, 2019. DOI: 10.1016/j.renene.2018.10.066.
  • A. Javed, B. K. Kasi, and F. A. Khan. “Predicting solar irradiance using machine learning techniques,” in 2019 15th Int. Wirel. Commun. Mob. Comput. Conf. IWCMC 2019, pp. 1458–62, 2019. DOI: 10.1109/IWCMC.2019.8766480.
  • D. S. Tripathy, B. R. Prusty, and D. Jena. “Short-term PV generation forecasting using quantile regression averaging,” in 2020 IEEE Int. Conf. Power Syst. Technol. POWERCON 2020, 2020. DOI: 10.1109/POWERCON48463.2020.9230535.
  • D. S. Tripathy, B. R. Prusty, and D. Jena. “Probabilistic forecasting of daily PV generation using quantile regression method,” in Proc. – 2020 IEEE India Counc. Int. Subsections Conf. INDISCON 2020, pp. 260–5, 2020. DOI: 10.1109/INDISCON50162.2020.00060.
  • C. Feng, M. Cui, B. Hodge, and J. Zhang, “A data-driven multi-model methodology with deep feature selection for short-term wind forecasting,” Appl. Energy, Vol. 190, pp. 1245–57, 2017. DOI: 10.1016/j.apenergy.2017.01.043.
  • A. Lahouar, and J. B. H. Slama, “Hour-ahead wind power forecast based on random forests,” Renew. Energy, 2017. DOI: 10.1016/j.renene.2017.03.064.
  • Y. Y. Hong, and T. R. A. Satriani, “Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network,” Energy, Vol. 209, pp. 118441, 2020. DOI: 10.1016/j.energy.2020.118441.
  • A. Chaudhary, A. Sharma, A. Kumar, K. Dikshit, and N. Kumar, “Short term wind power forecasting using machine learning techniques,” J. Stat. Manag. Syst., Vol. 23, no. 1, pp. 145–56, 2020. DOI: 10.1080/09720510.2020.1721632.
  • J. Zhang, H. Meng, B. Gu, and P. Li, “Research on short-term wind power combined forecasting and its Gaussian could uncertainty to support the integration of renewables and EVs,” Renew. Energy, Vol. 153, pp. 884–99, 2020. DOI: 10.1016/j.renene.2020.01.062.
  • M. E. K. Ali, M. Z. Hassan, A. B. M. S. Ali, and J. Kumar. “Prediction of Wind Speed Using Real Data: An Analysis of Statistical Machine Learning Techniques,” in Proc. – 2017 4th Asia-Pacific World Congr. Comput. Sci. Eng. APWC CSE 2017, pp. 259–64, 2018. DOI: 10.1109/APWConCSE.2017.00051.
  • T. Mahmoud, Z. Y. Dong, and J. Ma, “Advanced method for short-term wind power prediction with multiple observation points using extreme learning machines,” J. Eng., Vol. 2018, no. 1, pp. 29–38, 2018. DOI: 10.1049/joe.2017.0338.
  • Y. Zhang, P. Wang, C. Zhang, and S. Lei, “Wind energy prediction with LS-SVM based on Lorenz perturbation,” J. Eng., Vol. 2017, no. 13, pp. 1724–1727, 2017. DOI: 10.1049/joe.2017.0626.
  • Y. Li, P. Yang, and H. Wang, “Short-term wind speed forecasting based on improved ant colony algorithm for LSSVM,” Cluster. Comput., Vol. 22, pp. 11575–81, 2019. DOI: 10.1007/s10586-017-1422-2.
  • W. Energy, and T. Series, “‘Dust in the wind … ’, deep learning application to wind energy time series forecasting,” Energies, Vol. 12, pp. 1–20, 2019. DOI: 10.3390/en12122385.
  • S. Mujeeb, N. Javaid, H. Gul, N. Daood, S. Shabbir, and A. Arif, “Wind Power Forecasting Based on Efficient Deep Convolution Neural Networks" Advances on P2P, Parallel, Grid, Cloud and Internet Computing, 2020.
  • R. Jiao, X. Huang, L. Han, and W. Tian, “A model combining stacked auto encoder and back propagation algorithm for short-term wind power,” IEEE. Access., Vol. 3536, 2018. DOI: 10.1109/ACCESS.2018.2818108.
  • C. Huang, “A short-term wind speed forecasting model by using artificial neural networks with stochastic optimization for renewable energy systems,” Energies, Vol. 11, 2018. DOI: 10.3390/en11102777.
  • E. C. Eze, C. R. Chatwin, and S. Brighton, “Enhanced recurrent neural network for short-term wind farm power output prediction,” J. Appl. Sci., Vol. 2, pp. 28–35, 2019.
  • D. Dong, Z. Sheng, and T. Yang, “Wind power prediction based on recurrent neural network with long short-term memory units,” IEEE Int. Conf. Renew. Energy Power Eng., 34–38, 2018. DOI: 10.1109/REPE.2018.8657666.
  • Z. Wang, B. Wang, C. Liu, and W. Wang, “Improved BP neural network algorithm to wind power forecast,” J. Eng., Vol. 2017, no. 13, pp. 940–3, 2017. DOI: 10.1049/joe.2017.0469.
  • B. Aksoy, and R. Selbaş, “Estimation of wind turbine energy production value by using machine learning algorithms and development of implementation program,” Energy Sources, Part A Recover. Util. Environ. Eff., 1–13, 2019. DOI: 10.1080/15567036.2019.1631410.
  • M. Sharifzadeh, A. Sikinioti-lock, and N. Shah, “Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian process regression,” Renew. Sustain. Energy Rev., Vol. 108, pp. 513–38, 2019. DOI: 10.1016/j.rser.2019.03.040.
  • S. Harbola, and V. Coors, “One dimensional convolutional neural network architectures for wind prediction,” Energy Convers. Manag., Vol. 195, no. April, pp. 70–5, 2019. DOI: 10.1016/j.enconman.2019.05.007.
  • R. Yu, Z. Liu, X. Li, W. Lu, D. Ma, and M. Yu, “Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space,” Appl. Energy, Vol. 238, no. October 2018, pp. 249–57, 2019. DOI: 10.1016/j.apenergy.2019.01.010.
  • H. Demolli, A. S. Dokuz, A. Ecemis, and M. Gokcek, “Wind power forecasting based on daily wind speed data using machine learning algorithms,” Energy Convers. Manag., Vol. 198, no. March, pp. 111823, 2019. DOI: 10.1016/j.enconman.2019.111823.
  • B. Aksoy, “Estimation of energy produced in hydroelectric power plant industrial automation using deep learning and hybrid machine learning techniques,” Electr. Power Comp. Syst., Vol. 49, no. 3, pp. 213–32, 2021.
  • M. Sapitang, W. M. Ridwan, K. F. Kushiar, A. N. Ahmed, and A. El-Shafie, “Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy,” Sustain, Vol. 12, no. 15, 2020. DOI:10.3390/su12156121.
  • T. Guo, W. He, Z. Jiang, X. Chu, R. Malekian, and Z. Li, “An improved LSSVM model for intelligent prediction of the daily water level,” Energies, Vol. 12, no. 1, pp. 1–11, 2019. DOI: 10.3390/en12010112.
  • F. Üneş, M. Demirci, B. Taşar, Y. Z. Kaya, and H. Varçin, “Estimating dam reservoir level fluctuations using data-driven techniques,” Polish J. Environ. Stud., Vol. 28, no. 5, pp. 3451–62, 2019. DOI: 10.15244/pjoes/93923.
  • S. Zhang, L. Lu, J. Yu, and H. Zhou. “Short-term water level prediction using different artificial intelligent models,” in 5th Int. Conf. Agro-Geoinformatics, Agro-Geoinformatics 2016, no. July, 2016. DOI: 10.1109/Agro-Geoinformatics.2016.7577678.
  • A. Kavousi-Fard, and W. Su, “A combined prognostic model based on machine learning for tidal current prediction,” IEEE Trans. Geosci. Remote Sens., Vol. 55, no. 6, pp. 3108–14, 2017. DOI: 10.1109/TGRS.2017.2659538.
  • P. Gangwani, J. Soni, H. Upadhyay, and S. Joshi, “A deep learning approach for modeling of geothermal energy prediction,” Int. J. Comput. Sci. Inf. Secur., Vol. 18, no. 1, pp. 62–5, 2020.
  • D. Assouline, N. Mohajeri, A. Gudmundsson, and J. L. Scartezzini, “A machine learning approach for mapping the very shallow theoretical geothermal potential,” Geotherm. Energy, Vol. 7, no. 1, 2019. DOI: 10.1186/s40517-019-0135-6.
  • F. Elmaz, Ö Yücel, and A. Y. Mutlu, “Predictive modeling of biomass gasification with machine learning-based regression methods,” Energy, Vol. 191, pp. 116541, 2020. DOI: 10.1016/j.energy.2019.116541.
  • S. H. Samadi, B. Ghobadian, and M. Nosrati, “Prediction of higher heating value of biomass materials based on proximate analysis using gradient boosted regression trees method,” Energy Sources, Part A Recover. Util. Environ. Eff. 2019. DOI: 10.1080/15567036.2019.1630521.
  • E. E. Ozbas, D. Aksu, A. Ongen, M. A. Aydin, and H. K. Ozcan, “Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms,” Int. J. Hydrogen Energy, Vol. 44, no. 32, pp. 17260–68, 2019. DOI: 10.1016/j.ijhydene.2019.02.108.
  • T. Onsree, and N. Tippayawong, “Machine learning application to predict yields of solid products from biomass torrefaction,” Renew. Energy, Vol. 167, pp. 425–32, 2021. DOI: 10.1016/j.renene.2020.11.099.
  • L. R. Mansaray, K. Zhang, and A. S. Kanu, “Dry biomass estimation of paddy rice with Sentinel-1A satellite data using machine learning regression algorithms,” Comput. Electron. Agri., Vol. 176, no. May, pp. 105674, 2020. DOI: 10.1016/j.compag.2020.105674.
  • Z. Li, S. M. Mahbobur Rahman, R. Vega, and B. Dong, “A hierarchical approach using machine learning methods in solar photovoltaic energy production forecasting,” Energies, Vol. 9, no. 1, 2016. DOI: 10.3390/en9010055.
  • P. W. Khan, Y. C. Byun, S. J. Lee, D. H. Kang, J. Y. Kang, and H. S. Park, “Machine learning-based approach to predict energy consumption of renewable and nonrenewable power sources,” Energies, Vol. 13, no. 18, 2020. DOI: 10.3390/en13184870.
  • C. R. Sergiu Deitsch. Vincent Christlein, Stephan Berger, Claudia Buerhop-Lutz, Andreas Maier, Florian Gallwitz, “Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images,” 2018.
  • N. B. Ahmed Bouraiou, M. Hamouda, A. Chaker, A. Neçaibia, and M. Mostefaoui, “Experimental investigation of observed defects in crystalline silicon PV modules under outdoor hot dry climatic conditions in Algeria,” Sol. Energy, Vol. 159, pp. 475–87, 2018.
  • S. Meyera, et al., “Snail trails: Root cause analysis and test procedures,” Energy Procedia, Vol. 38, pp. 498–505, 2013.
  • S. Kajari-Schršder, I. Kunze, and M. Kšntges, “Criticality of cracks in PV modules,” Energy Procedia, Vol. 27, pp. 658–663, 2012.
  • P. I. D. S. G. We, M. One, P. I. D. E. Pid, “The five most common problems with solar panels,” pp. 1–2, 2017.
  • M. H. Ali, A. Rabhi, A. El Hajjaji, and G. M. Tina, “Real time fault detection in photovoltaic systems,” Energy Procedia, Vol. 111, pp. 914–23, 2017.
  • B. V. Dobaria, V. Sharma, and A. Adeshara, “Investigation of failure and degradation types of solar PV plants in a composite climate: abstract after 4–6, years of field operation,” Lect. Notes Electr. Eng., Vol. 435, pp. 227–35, 2018.
  • G. M. Lonkar, P. Klinkhachorn, U. B. Halabe, and H. V. S. GangaRao, “Automatic detection of subsurface defects using infrared thermography title,” AIP Conf. Proc., Vol. 760, no. 1, pp. 1469–76, 2005.
  • R. F. Colmenares-Quintero, E. R. Rojas-Martinez, F. Macho-Hernantes, K. E. Stansfield, and J. C. Colmenares-Quintero, “Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks,” Cogenteng, Vol. 8, no. 1, 2021. [Online]. DOI: 10.1080/23311916.2021.1981520.
  • A. S. Edun, “Finding faults in PV systems: Supervised and unsupervised dictionary learning With SSTDR,” IEEE Sens. J., Vol. 21, no. 4, pp. 4855–65, 2020.
  • A. S. Edun, et al., “Finding faults in PV systems: Supervised and unsupervised dictionary learning with SSTDR,” IEEE Sens. J., 1–11, 2020. DOI: 10.1109/JSEN.2020.3029707.
  • M. U. Ali, H. F. Khan, M. Masud, K. D. Kallu, and A. Zafar, “A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography,” Sol. Energy, Vol. 208, no. July, pp. 643–51, 2020. DOI: 10.1016/j.solener.2020.08.027.
  • J. Huang, R. Wai, S. Member, and G. Yang, “Transactions on power electronics design of hybrid artificial Bee colony algorithm and semi-supervised extreme learning machine for PV fault diagnoses by considering dust impact,” IEEE Trans. Power Electron., 2019. DOI: 10.1109/TPEL.2019.2956812.
  • J. Huang, R. Wai, S. Member, and W. E. I. Gao, “Newly-designed fault diagnostic method for solar photovoltaic generation system based on IV-curve measurement,” IEEE. Access., Vol. 7, pp. 70919–32, 2019. DOI: 10.1109/ACCESS.2019.2919337.
  • W. Junjie, G. Dedong, Z. Shaokang, W. Shan, and L. Haixiong, “Fault diagnosis method of photovoltaic array based on support vector machine,” Energy Sources, Part A Recover. Util. Environ. Eff., 2019. DOI: 10.1080/15567036.2019.1671557.
  • N. V. Sridharan, and V. Sugumaran, “Convolutional neural network based automatic detection of visible faults in a photovoltaic module,” Energy Sources, Part A Recover. Util. Environ. Eff., 2021. DOI: 10.1080/15567036.2021.1905753.
  • E. Pedersen, S. Rao, S. Katoch, K. Jaskie, A. Spanias, and C. Tepedelenlioglu. “PV Array Fault Detection using Radial Basis Networks,” in 2019 10th Int. Conf. Information, Intell. Syst. Appl., pp. 1–4, 2019.
  • V. S. B. Kurukuru, and M. A. Khan, “Fault classification for photovoltaic modules using thermography and machine learning techniques,” Int. Conf. Comput. Inf. Sci., 1–6, 2019. DOI: 10.1109/ICCISci.2019.8716442.
  • S. Rao, A. Spanias, and C. Tepedelenlioglu. “Solar array fault detection using neural networks,” in IEEE Int. Conf. Ind. Cyber Phys. Syst., pp. 196–200, 2019.
  • S. Katoch, G. Muniraju, S. Rao, and A. Spanias. “Shading prediction, fault detection, and consensus estimation for solar array control,” pp. 2–7.
  • F. Khondoker, S. Rao, A. Spanias, and C. Tepedelenlioglu. “Photovoltaic array simulation and fault prediction via multilayer perceptron models,” in 9th Int. Conf. Information, Intell. Syst. Appl., pp. 1–5, 2018.
  • Y. Tian, and C. Chen. “Design of photovoltaic array fault online evaluation system,” in 5th Int. Conf. Comput. Commun. Syst., pp. 912–916, 2020.
  • H. Momeni, N. Sadoogi, M. Farrokhifar, and H. F. Gharibeh, “Fault diagnosis in photovoltaic arrays using GBSSL method and proposing a fault correction,” IEEE Trans. Ind. Informatics, 1–9, 2019. DOI: 10.1109/TII.2019.2908992.
  • S. Liu, L. Dong, X. Liao, Y. Hao, X. Cao, and X. Wang, “A dilation and erosion-based clustering approach for fault diagnosis of photovoltaic arrays,” IEEE Sens. J., Vol. 19, no. 11, pp. 4123–37, 2019. DOI: 10.1109/JSEN.2019.2896236.
  • T. Babasaki, and Y. Higuchi. “Using PV string data to diagnose failure of solar panels in a solar power plant,” in IEEE Int. Telecommun. Energy Conf., pp. 1–4, 2018. DOI: 10.1109/INTLEC.2018.8612400.
  • S. Yoganand, and S. Chithra, “Proactive maintenance of small wind turbines using IoT and machine learning models,” Int. J. Green Energy, Vol. 19, no. 5, pp. 463–475, 2021. DOI: 10.1080/15435075.2021.1930004.
  • H. Rashid. “Fault prediction of wind turbine gearbox based on SCADA data and machine learning,” pp. 391–5, 2020.
  • L. Williams, C. Phillips, and S. Sheng. “Scalable wind turbine generator bearing fault prediction using machine learning: A case study,” 2020.
  • X. Yang, Y. Zhou, M. Yang, X. Zeng, and F. Yan. “Early warning method for bearing over-temperature fault of wind turbine based on Sparce Bayesian learning,” pp. 1405–10, 2020.
  • X. Chen, D. Lei, and G. Xu. “Prediction of icing fault of wind turbine blades based on deep learning,” in 2019 IEEE 2nd Int. Conf. Autom. Electron. Electr. Eng., pp. 295–9, 2019.
  • J. Ma, L. Ma, and X. Tian. “Wind turbine blade icing prediction based on deep belief network,” in 4th Int. Conf. Mech. Control Comput. Eng. Wind, pp. 26–9, 2019. DOI: 10.1109/ICMCCE48743.2019.00014.
  • A. Turnbull, J. Carroll, S. Koukoura, and A. McDonald, “Prediction of wind turbine generator bearing failure through analysis of high-frequency vibration data and the application of support vector machine algorithms,” J. Eng., Vol. 2019, no. 18, pp. 4965–9, 2019. DOI: 10.1049/joe.2018.9281.
  • J. Hsu, Y. Wang, K. Lin, M. Chen, and J. Hwai-yuan, “Wind turbine fault diagnosis and predictive maintenance through statistical process control and machine learning,” IEEE. Access., 2017. DOI: 10.1109/ACCESS.2020.2968615.
  • L. Chen, G. Xu, L. Liang, Q. Zhang, and S. Zhang. “Learning deep representation for blades icing fault detection of wind turbines,” in 2018 IEEE Int. Conf. Progn. Heal. Manag., pp. 1–8, 2018.
  • D. Zhang, L. Qian, B. Mao, C. A. N. Huang, and B. I. N. Huang, “A data-driven design for fault detection of wind turbines using random forests and XGboost,” IEEE. Access., Vol. 6, pp. 21020–31, 2018. DOI: 10.1109/ACCESS.2018.2818678.
  • Y. Si, L. Qian, B. Mao, and D. Zhang. “A data-driven approach for fault detection of offshore wind turbines using random forests,” pp. 3149–54, 2017.
  • B. S. Approach, R. M. Fernandez-canti, J. Blesa, S. Tornil-sin, and V. Puig, “Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach,” Annu. Rev. Control, Vol. 40, pp. 59–69, 2015.
  • B. I. Dadili Yassine. “Hybrid classifier for fault detection and isolation in wind turbine based on data-driven,” 2017.
  • X. Li, X. Shi, and P. Ding. “Research on feature extraction for rolling bearing fault detection in wind turbine,” pp. 5141–5, 2017.
  • Q. He, J. Zhao, G. Jiang, and P. Xie, “An unsupervised multi-view sparse filtering approach for current-based wind turbine gearbox fault diagnosis,” IEEE Trans. Instrum. Meas., Vol. 9456, pp. 1–10, 2020. DOI: 10.1109/TIM.2020.2964064.
  • X. Zhang, P. Han, L. I. Xu, Y. Wang, and L. U. Gao, “Research on bearing fault diagnosis of wind turbine gearbox based on 1DCNN-PSO-SVM,” IEEE. Access., Vol. 8, pp. 192248–58, 2020. DOI: 10.1109/ACCESS.2020.3032719.
  • L. Lu, Y. He, T. Wang, T. Shi, and Y. Ruan, “Wind turbine planetary gearbox fault diagnosis based on self-powered wireless sensor and deep learning approach,” IEEE. Access., 2017. DOI: 10.1109/ACCESS.2019.2936228.
  • L. Cao, S. Member, and Z. Qian. “Fault diagnosis of wind turbine gearbox based on deep bi-directional long short-term memory under time-varying non-stationary operating conditions,” IEEE Access, pp. 1–9, 2019. DOI: 10.1109/ACCESS.2019.2947501.
  • G. Jiang, H. He, J. Yan, and P. Xie, “Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox,” IEEE Trans. Ind. Electron., Vol. 66, no. 4, pp. 3196–207, 2018. DOI: 10.1109/TIE.2018.2844805.
  • A. Shahab, and M. P. Singh. “Comparative analysis of different machine learning algorithms in classification of suitability of renewable energy resource,” in Int. Conf. Commun. Signal Process., pp. 360–4.
  • R. Rastogi, R. Jaiswal, and R. K. Jaiswal, “Renewable energy firm’s performance analysis using machine learning approach,” Procedia Comput. Sci, Vol. 175, pp. 500–7, 2020. DOI: 10.1016/j.procs.2020.07.071.
  • M. Suzuki, M. Ito, and R. Takashima. “Evaluating renewable energy policies using a multi-agent reinforcement learning model,” in IEEE Int. Conf. Syst. Man, Cybern. SMC 2018, pp. 959–63, 2018. DOI: 10.1109/SMC.2018.00170.
  • T. W. David, H. Anizelli, T. J. Jacobsson, C. Gray, W. Teahan, and J. Kettle, “Enhancing the stability of organic photovoltaics through machine learning,” Nano Energy, Vol. 78, no. September, pp. 105342, 2020. DOI: 10.1016/j.nanoen.2020.105342.
  • H. A. Kazem, J. Yousif, M. T. Chaichan, and A. H. A. Al-Waeli, “Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems,” Int. J. Energy Res., Vol. 43, no. 14, pp. 8572–91, 2019. DOI: 10.1002/er.4855.
  • K. Li, et al. “Photovoltaic plant operating statuses identification model based on support vector machine using loss quantity of electricity feature parameters,” in IET Conf. Publ., 2015. DOI: 10.1049/cp.2015.0504.
  • M. Takruri, M. Farhat, O. Barambones, J. A. Ramos-hernanz, and M. A. Sakur, “Maximum power point tracking of PV system based on machine learning,” Energies, Vol. 13, no. 692, pp. 1–14, 2020.
  • Y. Liu, et al., “Evaluating smart grid renewable energy accommodation capability with uncertain generation using deep reinforcement learning Yongnan,” Futur. Gener. Comput. Syst., 2019. DOI: 10.1016/j.future.2019.09.036.
  • T. Sogabe, I. Haruhisa, K. Sakamoto, K. Yamaguchi, M. Sogabe, and T. Sato, “Optimization of decentralized renewable energy system by weather forecasting and deep machine learning techniques,” IEEE Innov. Smart Grid Technol. - Asia, 1014–1018, 2016. DOI: 10.1109/ISGT-Asia.2016.7796524.

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