75
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Enhancing Solar Energy Generation: A Comprehensive Machine Learning-Based PV Prediction and Fault Analysis System for Real-Time Tracking and Forecasting

&
Pages 1497-1512 | Received 01 Sep 2023, Accepted 30 Nov 2023, Published online: 27 Dec 2023

References

  • Y. Cui, et al., “Single‐junction organic photovoltaic cell with 19% efficiency,” Adv. Mater., vol. 33, no. 41, p. e2102420, 2021. DOI: 10.1038/s41563-022-01244-y.
  • A. Bruck, S. D. Ruano and H. Auer, “One piece of the puzzle towards 100 Positive Energy Districts (PEDs) across Europe by 2025: an open-source approach to unveil favourable locations of PV-based PEDs from a techno-economic perspective,” Energy, vol. 254, p. 124152, 2022. DOI: 10.1016/j.energy.2022.124152.
  • B. Seo, J. Y. Kim and J. Chung, “Overview of global status and challenges for end-of-life crystalline silicon photovoltaic panels: a focus on environmental impacts,” Waste Manage., vol. 128, pp. 45–54, 2021. DOI: 10.1016/j.wasman.2021.04.045.
  • K. Y. Lau, C. W. Tan and K. Y. Ching, “The implementation of grid-connected, residential rooftop photovoltaic systems under different load scenarios in Malaysia,” J. Clean. Product., vol. 316, p. 128389, 2021. DOI: 10.1016/j.jclepro.2021.128389.
  • A. Agga, A. Abbou, M. Labbadi, Y. El Houm and I. H. O. Ali, “CNN-LSTM: an efficient hybrid deep learning architecture for predicting short-term photovoltaic power production,” Electr. Power Syst. Res., vol. 208, p. 107908, 2022. DOI: 10.1016/j.epsr.2022.107908.
  • Q. Hassan, “Evaluation and optimization of off-grid and on-grid photovoltaic power system for typical household electrification,” Renewable Energy, vol. 164, pp. 375–390, 2021. DOI: 10.1016/j.renene.2020.09.008.
  • S. Li, et al., “Effects of the center units of small‐molecule donors on the morphology, photovoltaic performance, and device stability of all‐small‐molecule organic solar cells,” Solar RRL, vol. 5, no. 10, pp. 2100515, 2021. pp. DOI: 10.1002/solr.202100515.
  • M. Abdel-Basset, H. Hawash, R. K. Chakrabortty and M. Ryan, “PV-Net: an innovative deep learning approach for efficient forecasting of short-term photovoltaic energy production,” J Clean. Prod., vol. 303, p. 127037, 2021. DOI: 10.1016/j.jclepro.2021.127037.
  • M. Guermoui, K. Bouchouicha, N. Bailek and J. W. Boland, “Forecasting intra-hour variance of photovoltaic power using a new integrated model,” Energy Convers. Manage, vol. 245, p. 114569, 2021. DOI: 10.1016/j.enconman.2021.114569.
  • J. Caballero-Peña, C. Cadena-Zarate, A. Parrado-Duque and G. Osma-Pinto, “Distributed energy resources on distribution networks: a systematic review of modelling, simulation, metrics, and impacts,” Int. Electr. Power Energy Syst., vol. 138, p. 107900, 2022. DOI: 10.1016/j.ijepes.2021.107900.
  • 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. 46117–46128, 2021. DOI: 10.1109/ACCESS.2021.3066494.
  • M. Mishra, P. B. Dash, J. Nayak, B. Naik and S. K. Swain, “Deep learning and wavelet transform integrated approach for short-term solar PV power prediction: measurement,” Measurement, vol. 166, pp. 108250, 2020. DOI: 10.1016/j.measurement.2020.108250.
  • F. Wang, Z. Xuan, Z. Zhen, K. Li, T. Wang and M. Shi, “A day-ahead PV power forecasting method based on the LSTM-RNN model and time correlation modification under a partial daily pattern prediction framework,” Energy Convers. Manage., vol. 212, p. 112766, 2020. DOI: 10.1016/j.enconman.2020.112766.
  • D. Lee and K. Kim, “PV power prediction in a peak zone using recurrent neural networks without future meteorological information,” Renewable Energy, vol. 173, pp. 1098–1110, 2021. DOI: 10.1016/j.renene.2020.12.021.
  • P. Malik, R. Chandel and S. S. Chandel, “A power prediction model and its validation for a rooftop photovoltaic power plant considering module degradation,” Sol. Energy, vol. 224, pp. 184–194, 2021. DOI: 10.1016/j.solener.2021.06.015.
  • H. Eom, Y. Son and S. Choi, “Feature-selective ensemble learning-based long-term regional PV generation forecasting,” IEEE Access, vol. 8, pp. 54620–54630, 2020. DOI: 10.1109/ACCESS.2020.2981819.
  • G. Q. Lin, L. L. Li, M. L. Tseng, H. M. Liu, D. D. Yuan and R. R. Tan, “An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation,” J. Clean. Prod., vol. 253, pp. 119966, 2020. DOI: 10.1016/j.jclepro.2020.119966.
  • Y. Zhou, N. Zhou, L. Gong and M. Jiang, “Prediction of photovoltaic power output based on similar day analysis, genetic algorithm, and extreme learning machine,” Energy, vol. 204, p. 117894, 2020. DOI: 10.1016/j.energy.2020.117894.
  • S. Zhang, J. Wang, H. Liu, J. Tong and Z. Sun, “Prediction of energy photovoltaic power generation based on artificial intelligence algorithm,” Neural Comput. Appl., vol. 33, no. 3, pp. 821–835, 2021. DOI: 10.1007/s00521-020-05249-z.
  • D. Kim, D. Kwon, L. Park, J. Kim and S. Cho, “Multiscale LSTM-based deep learning for very short-term photovoltaic power generation forecasting in innovative city energy management,” IEEE Syst. J., vol. 15, no. 1, pp. 346–354, 2021. DOI: 10.1109/JSYST.2020.3007184.
  • A. Rajaram and K. Sathiyaraj, “An improved optimization technique for energy harvesting system with grid connected power for green house management,” J. Electr. Eng. Technol., vol. 17, no. 5, pp. 2937–2949, 2022. DOI: 10.1007/s42835-022-01033-2.
  • P. Ashok Babu, J. L. Mazher Iqbal, S. Siva Priyanka, M. Jithender Reddy, G. Sunil Kumar and A. Rajaram, “Power control and optimization for power loss reduction using deep learning in microgrid systems,” Electr. Power Compon. Syst., pp. 1–14, 2023. DOI: 10.1080/15325008.2023.2217175.
  • H. Shekhar, et al., “Demand side control for energy saving in renewable energy resources using deep learning optimization,” Electr. Power Compon. Syst., vol. 51, no. 19, pp. 2397–2413, 2023. DOI: 10.1080/15325008.2023.2246463.
  • S. Sripadmanabhan Indira, C. Aravind Vaithilingam, R. Sivasubramanian, K. K. Chong, K. Narasingamurthi and R. Saidur, “Prototype of a novel hybrid concentrator Photovoltaic and solar thermoelectric generator system for outdoor study,” Renewable Energy, vol. 201, pp. 224–239, 2022. DOI: 10.1016/j.renene.2022.10.110.
  • K. Çelik, M. Demirtas and N. Öztürk, “Analytical MPPT control and comparative analysis for PV panel connected to DC microgrid,” Electr. Power Compon. Syst., vol. 51, no. 11, pp. 1075–1088, 2023. DOI: 10.1080/15325008.2023.2189759.
  • I. Bodur, E. Celik and N. Ozturk, “A short-term load demand forecasting based on the method of LSTM,” Int. Conf. Renewable Energy Res. Appl., pp. 171–174, 2021. DOI: 10.1109/ICRERA52334.2021.9598773.
  • D. S. Tripathy, B. R. Prusty and K. Bingi, “A k‐nearest neighbor‐based averaging model for probabilistic PV generation forecasting,” Int. J. Numer. Model., vol. 35, no. 2, p. e2983, 2022. DOI: 10.1002/jnm.2983.
  • I. A. A. Amra and A. Y. Maghari, “Students performance prediction using KNN and Naïve Bayesian,” Int. Conf. Inf. Technol., pp. 909–913, 2017. DOI: 10.1109/ICITECH.2017.8079967.
  • M. Farsi, et al., “Prediction of oil flow rate through orifice flow meters: optimized machine-learning techniques,” Measurement, vol. 174, p. 108943, 2022. DOI: 10.1016/j.measurement.2020.108943.
  • A. A. Alsakati, C. A. Vaithilingam, K. Naidu, G. Rajendran, J. Alnasseir and A. Jagadeeshwaran, “Particle swarm optimization for tuning power system stabilizer towards transient stability improvement in power system network,” IEEE Int. Conf. Artif. Intell. Eng. Technol., pp. 1–6, 2021. DOI: 10.1109/IICAIET51634.2021.9573534.
  • R. Senthilkumar, G. M. Tamilselvan, S. Kanithan and N. Arun Vignesh, “Routing in WSNs powered by a hybrid energy storage system through a CEAR protocol based on cost welfare and route score metric,” Int. J. Comput. Commun., vol. 14, no. 2, pp. 233–252, 2019. DOI: 10.15837/ijccc.2019.2.3184.
  • http://www.dkasc.ac.in
  • M. T. Hajibeigy, R. Walvekar and C. V. Aravind, “Mathematical modelling, simulation analysis of a photovoltaic thermal system,” J. Therm. Eng., vol. 7, no. 1, pp. 291–306, 2021. https://jten.yildiz.edu.tr/storage/upload/pdfs/1628601995-en.pdf. DOI: 10.18186/thermal.850645.
  • R. H. F. Alves, G. A. de Deus Junior, E. G. Marra and R. P. Lemos, “Automatic fault classification in photovoltaic modules using Convolutional Neural Networks,” Renewable Energy, vol. 179, pp. 502–516, 2021. DOI: 10.1016/j.renene.2021.07.070.
  • M. Le, V. S. Luong, D. K. Nguyen, V.-D. Dao, N. H. Vu and H. H. T. Vu, “Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network,” Sustain. Energy Technol. Assess., vol. 48, p. 101545, 2021. DOI: 10.1016/j.seta.2021.101545.
  • J. Wang, D. Gao, S. Zhu, S. Wang and H. Liu, “Fault diagnosis method of photovoltaic array based on support vector machine,” Energy Sources Part A: Recovery Util. Environ. Eff., vol. 45, no. 2, pp. 5380–5395, 2023. DOI: 10.1080/15567036.2019.1671557.
  • F. Kaya, G. Şahin and M. H. Alma, “Investigation effects of environmental and operating factors on PV panel efficiency using by multivariate linear regression,” Int. J. Energy Res., vol. 45, no. 1, pp. 554–567, 2021. DOI: 10.1002/er.5717.
  • P. K. Ganti, H. Naik and M. K. Barada, “Environmental impact analysis and enhancement of factors affecting the photovoltaic (PV) energy utilization in mining industry by sparrow search optimization based gradient boosting decision tree approach,” Energy, vol. 244, p. 122561, 2022. DOI: 10.1016/j.energy.2021.122561.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.