Journal Home > Volume 3

Integrating artificial intelligence (AI) into photovoltaic (PV) systems has become a revolutionary approach to improving the efficiency, reliability, and predictability of solar power generation. In this paper, we explore the impact of AI technology on PV power generation systems and its applications from a global perspective. Central to the discussion are the pivotal applications of AI in maximum power point tracking (MPPT), power forecasting, and fault detection within the PV system. On the one hand, the integration with AI technology enables the optimization and improvement of the operational efficiency of PV systems. On the other hand, new challenges have been observed, mainly in the areas of data processing and model management. Moreover, advances in AI technology and hardware upgrades will lead to the rapid global popularization of new energy sources such as solar energy, which is expected to replace traditional energy sources. Finally, we describe forward-looking solutions including transfer learning, few-shot learning, and edge computing, as well as the state of the art.


menu
Abstract
Full text
Outline
About this article

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Show Author's information Jiaming Hu1Boon-Han Lim2Xiaoyun Tian1( )Kang Wang1Dachuan Xu1Feng Zhang3Yong Zhang4
Institute of Operations Research and Information Engineering, Beijing University of Technology, Beijing 100124, China
Department of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Malaysia
College of Mathematics and Information Science, Hebei University, Baoding 071002, China
Shenzhen Institutes of Advanced Technology of the Chinese Academy of Science, Shenzhen 518055, China

Abstract

Integrating artificial intelligence (AI) into photovoltaic (PV) systems has become a revolutionary approach to improving the efficiency, reliability, and predictability of solar power generation. In this paper, we explore the impact of AI technology on PV power generation systems and its applications from a global perspective. Central to the discussion are the pivotal applications of AI in maximum power point tracking (MPPT), power forecasting, and fault detection within the PV system. On the one hand, the integration with AI technology enables the optimization and improvement of the operational efficiency of PV systems. On the other hand, new challenges have been observed, mainly in the areas of data processing and model management. Moreover, advances in AI technology and hardware upgrades will lead to the rapid global popularization of new energy sources such as solar energy, which is expected to replace traditional energy sources. Finally, we describe forward-looking solutions including transfer learning, few-shot learning, and edge computing, as well as the state of the art.

Keywords: artificial intelligence, neural networks, solar photovoltaic system, meta-heuristic algorithm

References(159)

[1]

B. Dubey, S. Agrawal, and A. K. Sharma, India’s renewable energy portfolio: An investigation of the untapped potential of RE, policies, and incentives favoring energy security in the country, Energies, vol. 16, no. 14, p. 5491, 2023.

[2]

Y. Guo, Y. Yang, M. Bradshaw, C. Wang, and M. Blondeel, Globalization and decarbonization: Changing strategies of global oil and gas companies, Wires Clim. Change, vol. 14, no. 6, pp. e849, 2023.

[3]

Y. Wang, R. Wang, K. Tanaka, P. Ciais, J. Penuelas, Y. Balkanski, J. Sardans, D. Hauglustaine, W. Liu, X. Xing, et al., Accelerating the energy transition towards photovoltaic and wind in China, Nature, vol. 619, no. 7971, pp. 761–767, 2023.

[4]

Y. Hua, M. Oliphant, and E. J. Hu, Development of renewable energy in Australia and China: A comparison of policies and status, Renew. Energy, vol. 85, pp. 1044–1051, 2016.

[5]

J. Liu and D. Goldstein, Understanding China’s renewable energy technology exports, Energy Policy, vol. 52, pp. 417–428, 2013.

[6]
IEA, Renewables 2021, https://www.iea.org/reports/renewables-2021, 2021.
[7]
BP p. l. c., Statistical review of world energy 2020, https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2020-full-report.pdf, 2020.
[8]
BP b. l. c., BP energy outlook, https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2023.pdf, 2023.
[9]

D. S. Painter, Oil and geopolitics: The oil crises of the 1970s and the cold war, Hist. Soc. Res., vol. 39, no. 4, pp. 186–203, 2014.

[10]

F. Tuna, A political assessment of the effect of Russian-Ukrainian war on the energy markets, J. Financ. Econ. Bank., vol. 3, no. 2, pp. 73–76, 2022.

[11]
J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge, MA, USA: MIT Press, 1992.
DOI
[12]
L. Zhang, Y. Bai, and A. Al-Amoudi, GA-RBF neural network based maximum power point tracking for grid-connected photovoltaic systems, in Proc. 2002 Int. Conf. Power Electronics Machines and Drives, Sante Fe, NM, USA, 2002, pp. 18–23.
DOI
[13]
S. Daraban, D. Petreus, and C. Morel, A novel global MPPT based on genetic algorithms for photovoltaic systems under the influence of partial shading, in Proc. IECON 2013 - 39th Annual Conf. IEEE Industrial Electronics Society, Vienna, Austria, 2013, pp. 1490–1495.
DOI
[14]
P. Megantoro, Y. D. Nugroho, F. Anggara, Suhono, and E. Y. Rusadi, Simulation and characterization of genetic algorithm implemented on MPPT for PV system under partial shading condition, in Proc. 3rd Int. Conf. Information Technology, Information System and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, 2018, pp. 74–78.
DOI
[15]

A. Harrag and S. Messalti, Variable step size modified P&O MPPT algorithm using GA-based hybrid offline/online PID controller, Renew. Sustain. Energy Rev., vol. 49, pp. 1247–1260, 2015.

[16]

A. Borni, T. Abdelkrim, N. Bouarroudj, A. Bouchakour, L. Zaghba, A. Lakhdari, and L. Zarour, Optimized MPPT controllers using GA for grid connected photovoltaic systems, comparative study, Energy Procedia, vol. 119, pp. 278–296, 2017.

[17]

A. Feroz Mirza, M. Mansoor, Q. Ling, M. I. Khan, and O. M. Aldossary, Advanced variable step size incremental conductance MPPT for a standalone PV system utilizing a GA-tuned PID controller, Energies, vol. 13, no. 16, p. 4153, 2020.

[18]
A. Badis, M. N. Mansouri, and M. H. Boujmil, A genetic algorithm optimized MPPT controller for a PV system with DC-DC boost converter, in Proc. 2017 Int. Conf. Engineering & MIS (ICEMIS), Monastir, Tunisia, 2017, pp. 1–6.
DOI
[19]

M. Lasheen, A. K. Abdel Rahman, M. Abdel-Salam, and S. Ookawara, Performance enhancement of constant voltage based MPPT for photovoltaic applications using genetic algorithm, Energy Procedia, vol. 100, pp. 217–222, 2016.

[20]
A. E. S. A. Nafeh, A modified MPPT control loop for PV/battery-charging system using PI controller optimally tuned with GA, Int. J. Numer. Model. Electron. Netw. Devices Fields, vol. 24, no. 2, pp. 111–122, 2011.
DOI
[21]

R. Ramaprabha and B. L. Mathur, Intelligent Controller based Maximum Power Point Tracking for Solar PV System, Int. J. Comput. Appl., vol. 12, no. 10, pp. 37–42, 2011.

[22]

Z. Salam, J. Ahmed, and B. S. Merugu, The application of soft computing methods for MPPT of PV system: A technological and status review, Appl. Energy, vol. 107, pp. 135–148, 2013.

[23]

K. Sundareswaran, S. Peddapati and S. Palani, MPPT of PV systems under partial shaded conditions through a colony of flashing fireflies, IEEE Trans. Energy Convers., vol. 29, no. 2, pp. 463–472, 2014.

[24]

C. Hemalatha, Rajkumar M. V., and G. V. Krishnan. Simulation and analysis of MPPT control with modified firefly algorithm for photovoltaic system, Int. J. Innov. Stud. Sci. Eng. Technol., vol. 2, no. 11, pp. 48–52, 2016.

[25]

R. B. Watanabe, O. H. Ando Jr, P. G. M. Leandro, F. Salvadori, M. F. Beck, K. Pereira, M. H. M. Brandt, and F. M. de Oliveira, Implementation of the bio-inspired metaheuristic firefly algorithm (FA) applied to maximum power point tracking of photovoltaic systems, Energies, vol. 15, no. 15, p. 5338, 2022.

[26]
B. Bilal, Implementation of Artificial Bee Colony algorithm on Maximum Power Point Tracking for PV modules, in Proc. 8th Int. Symp. Advanced Topics in Electrical Engineering (ATEE), Bucharest, Romania, 2013, pp. 1–4.
DOI
[27]
H. Salmi, Maximum power point tracking (MPPT) using artificial bee colony based algorithm for photovoltaic system, Int. J. Intell. Inf. Syst., vol. 5, no. 1, p. 1, 2016.
DOI
[28]

C. González-Castaño, C. Restrepo, S. Kouro, and J. Rodriguez, MPPT algorithm based on artificial bee colony for PV system, IEEE Access, vol. 9, pp. 43121–43133, 2021.

[29]

F. Salem, M. A. Moteleb, and H. T. Dorrah, An enhanced fuzzy-PI controller applied to the MPPT problem, J. Sci. Eng., vol. 8, no. 2, pp. 147–153, 2005.

[30]

K. Ishaque, Z. Salam, M. Amjad, and S. Mekhilef, An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation, IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3627–3638, 2012.

[31]
H. Renaudineau, F. Donatantonio, J. Fontchastagner, G. Petrone, G. Spagnuolo, J. P. Martin, and S. Pierfederici, A PSO-based global MPPT technique for distributed PV power generation, IEEE Trans. Ind. Electron., vol. 62, no. 2, pp. 1047–1058, 2015.
DOI
[32]
N. Ahmad Kamal, A. T. Azar, G. S. Elbasuony, K. M. Almustafa, and D. Almakhles, PSO-based adaptive perturb and observe MPPT technique for photovoltaic systems, in Proc. Int. Conf. Advanced Intelligent Systems and Informatics 2019 (AISI2019), Cairo, Egypt, 2019, pp. 125–135.
DOI
[33]
Z. Cheng, H. Zhou, and H. Yang, Research on MPPT control of PV system based on PSO algorithm, in Proc. Chinese Control and Decision Conf., Xuzhou, China, 2010, pp. 887–892.
DOI
[34]

E. S. Wirateruna, M. J. Afroni, and A. F. Ayu, Implementation of PSO algorithm on MPPT PV system using Arduino Uno under PSC, Int. J. Artif. Intell. Robot., vol. 5, no. 1, pp. 13–20, 2023.

[35]
V. Phimmasone, Y. Kondo, T. Kamejima, and M. Miyatake, Evaluation of extracted energy from PV with PSO-based MPPT against various types of solar irradiation changes, in Proc. 2010 Int. Conf. Electrical Machines and Systems, Incheon, Republic of Korea, 2010, pp. 487–492.
[36]
C. L. Liu, Y. F. Luo, J. W. Huang, and Y. H. Liu, A PSO-based MPPT algorithm for photovoltaic systems subject to inhomogeneous insolation, in Proc. 6th Int. Conf. Soft Computing and Intelligent Systems, and 13th Int. Symp. Advanced Intelligence Systems, Kobe, Japan, 2012, pp. 721–726.
DOI
[37]

P. S. Gavhane, S. Krishnamurthy, R. Dixit, J. P. Ram, and N. Rajasekar, EL-PSO based MPPT for Solar PV under Partial Shaded Condition, Energy Procedia, vol. 117, pp. 1047–1053, 2017.

[38]

W. Hayder, E. Ogliari, A. Dolara, A. Abid, M. Ben Hamed, and L. Sbita, Improved PSO: A comparative study in MPPT algorithm for PV system control under partial shading conditions, Energies, vol. 13, no. 8, p. 2035, 2020.

[39]
A. H. Besheer and M. Adly, Ant colony system based PI maximum power point tracking for stand alone photovoltaic system, in Proc. IEEE Int. Conf. Industrial Technology, Athens, Greece, 2012, pp. 693–698.
DOI
[40]

S. Titri, C. Larbes, K. Y. Toumi, and K. Benatchba, A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions, Appl. Soft Comput., vol. 58, pp. 465–479, 2017.

[41]
S. K. Sahoo, M. Balamurugan, S. Anurag, R. Kumar, and V. Priya, Maximum power point tracking for PV panels using ant colony optimization, in Proc. Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, 2017, pp. 1–4.
DOI
[42]
N. H. Saad, A. A. Sattar and A. M. Mansoar, Artificial neural controller for maximum power point tracking ofphotovoltaic system, in Proc. 2006 11th Int. Middle East Power Systems Conf., El-Minia, Egypt, 2006, pp. 562–567.
[43]

M. Abd Kadir and S. Sharifah, Development of artificial neural network based MPPT for photovoltaic system during shading condition, Appl. Mech. Mater., vol. 448-453, pp. 1573–1578, 2013.

[44]

C. Jaiswal and D. K. Singh, Simulation & modelling of standalone PV system using feed forward neural network, Int. J. Sci. Eng. Technol. Res., vol. 6, no. 19, pp. 3765–3768, 2017.

[45]

A. Mellit and S. A. Kalogirou, Artificial intelligence techniques for photovoltaic applications: A review, Prog. Energy Combust. Sci., vol. 34, no. 5, pp. 574–632, 2008.

[46]

M. Derbeli, C. Napole, O. Barambones, J. Sanchez, I. Calvo, and P. Fernández-Bustamante, Maximum power point tracking techniques for photovoltaic panel: A review and experimental applications, Energies, vol. 14, no. 22, p. 7806, 2021.

[47]
K. Ali Mohammad and S. M. Musa, Optimization of solar energy using recurrent neural network controller, in Proc. 14th Int. Conf. Computational Intelligence and Communication Networks (CICN), Al-Khobar, Saudi Arabia, 2022, pp. 1–6.
DOI
[48]

L. Avila, M. De Paula, I. Carlucho, and C. Sanchez Reinoso, MPPT for PV systems using deep reinforcement learning algorithms, IEEE Lat. Am. Trans., vol. 17, no. 12, pp. 2020–2027, 2019.

[49]
B. C. Phan, Y. C. Lai, and C. E. Lin, A deep reinforcement learning-based MPPT control for PV systems under partial shading condition, Sensors, vol. 20, no. 11, p. 3039, 2020.
DOI
[50]

L. Avila, M. De Paula, M. Trimboli, and I. Carlucho, Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids, Appl. Soft Comput., vol. 97, p. 106711, 2020.

[51]
N. Khaehintung, P. Sirisuk, and W. Kurutach, A novel ANFIS controller for maximum power point tracking in photovoltaic systems, in Proc. 5th Int. Conf. Power Electronics and Drive Systems, Singapore, 2003, pp. 833–836.
[52]
K. Amara, A. Fekik, D. Hocine, M. L. Bakir, E. B. Bourennane, T. Ali Malek, and A. Malek, Improved Performance of a PV Solar Panel with Adaptive Neuro Fuzzy Inference System ANFIS based MPPT, in Proc. 7th Int. Conf. Renewable Energy Research and Applications (ICRERA), Paris, France, 2018, pp. 1098–1101.
DOI
[53]

R. T. Moyo, P. Y. Tabakov, and S. Moyo, Design and modeling of the ANFIS-based MPPT controller for a solar photovoltaic system, J. Sol. Energy Eng., vol. 143, no. 4, p. 041002, 2021.

[54]

M. S. Aït Cheikh, C. Larbes, G. F. Tchoketch Kebir, and A. Zerguerras, Maximum power point tracking using a fuzzy logic control scheme, J. Ren. Energies, vol. 10, no. 3, pp. 387–395, 2007.

[55]
A. K. Pandey, V. Singh, and S. Jain, Study and comparative analysis of perturb and observe (P&O) and fuzzy logic based PV-MPPT algorithms, in Applications of AI and IOT in Renewable Energy, R. N. Shaw, A. Ghosh, S. Mekhilef, and V. E. Balas Eds. Amsterdam, The Netherlands: Elsevier, 2022. pp. 193–209.
DOI
[56]

K. W. Nasser, S. J. Yaqoob, and Z. A. Hassoun, Improved dynamic performance of photovoltaic panel using fuzzy Logic-MPPT algorithm, Indones. J. Electr. Eng. Comput. Sci., vol. 21, no. 2, p. 617, 2021.

[57]

S. Makhloufi and R. Abdessemed, Type-2 fuzzy logic optimum PV/inverter sizing ratio for grid-connected PV systems: Application to selected Algerian locations, J. Electr. Eng. Technol., vol. 6, no. 6, pp. 731–741, 2011.

[58]
S. Soltani and M. J. Kouhanjani, Fuzzy logic type-2 controller design for MPPT in photovoltaic system, in Proc. Conf. Electrical Power Distribution Networks Conf. (EPDC), Semnan, Iran, 2017, pp. 149–155.
DOI
[59]

B. Meryem, Photovoltaic power control using fuzzy logic and fuzzy logic type 2 MPPT algorithms and buck converter, Adv. Technol. Innov., vol. 4, no. 3, pp. 125–139, 2019.

[60]

C. S. Chiu, T-S fuzzy maximum power point tracking control of solar power generation systems, IEEE Trans. Energy Convers., vol. 25, no. 4, pp. 1123–1132, 2010.

[61]

H. Abid, A. Toumi, and M. Chaabane, MPPT algorithm for photovoltaic panel based on augmented takagi-sugeno fuzzy model, ISRN Renew. Energy, vol. 2014, p. 253146, 2014.

[62]

H. Khabou, M. Souissi, and A. Aitouche, MPPT implementation on boost converter by using T–S fuzzy method, Math. Comput. Simul., vol. 167, pp. 119–134, 2020.

[63]

A. Rezvani, M. Izadbakhsh, M. Gandomkar, and S. Vafaei, Implementing GA-ANFIS for maximum power point tracking in PV system, Indian J. Sci. Technol., vol. 8, no. 10, p. 982, 2015.

[64]

N. Priyadarshi, S. Padmanaban, J. B. Holm-Nielsen, F. Blaabjerg, and M. S. Bhaskar, An experimental estimation of hybrid ANFIS–PSO-based MPPT for PV grid integration under fluctuating Sun irradiance, IEEE Syst. J., vol. 14, no. 1, pp. 1218–1229, 2020.

[65]

S. S. Mohammed, D. Devaraj, and T. P. Imthias Ahamed, GA-optimized fuzzy-based MPPT technique for abruptly varying environmental conditions, J. Inst. Eng. Ind. Ser. B, vol. 102, no. 3, pp. 497–508, 2021.

[66]

K. H. Chao and M. N. Rizal, A hybrid MPPT controller based on the genetic algorithm and ant colony optimization for photovoltaic systems under partially shaded conditions, Energies, vol. 14, no. 10, p. 2902, 2021.

[67]
R. Ramaprabha, V. Gothandaraman, K. Kanimozhi, R. Divya, and B. L. Mathur, Maximum power point tracking using GA-optimized artificial neural network for Solar PV system, in Proc. 1st Int. Conf. Electrical Energy Systems, Chennai, India, 2011, pp. 264–268.
DOI
[68]

A. V. Prathaban and D. Karthikeyan, Grey wolf optimization-recurrent neural network based maximum power point tracking for photovoltaic application, Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 2, p. 629, 2022.

[69]

B. Babes, A. Boutaghane, and N. Hamouda, A novel nature-inspired maximum power point tracking (MPPT) controller based on ACO-ANN algorithm for photovoltaic (PV) system fed arc welding machines, Neural Comput. Appl., vol. 34, no. 1, pp. 299–317, 2022.

[70]

A. A. Firdaus, R. T. Yunardi, E. I. Agustin, S. D. N. Nahdliyah, and T. A. Nugroho, An improved control for MPPT based on FL-PSo to minimize oscillation in photovoltaic system, Int. J. Power Electron. Drive Syst. IJPEDS, vol. 11, no. 2, p. 1082, 2020.

[71]

Y. K. Semero, D. Zheng, and J. Zhang, A PSO-ANFIS based Hybrid Approach for Short Term PV Power Prediction in Microgrids, Electr. Power Compon. Syst., vol. 46, no. 1, pp. 95–103, 2018.

[72]

S. C. Lim, J. H. Huh, S. H. Hong, C. Y. Park, and J. C. Kim, Solar power forecasting using CNN-LSTM hybrid model, Energies, vol. 15, no. 21, p. 8233, 2022.

[73]
D. A. R. de Jesús, P. Mandal, S. Chakraborty, and T. Senjyu, Solar PV power prediction using A new approach based on hybrid deep neural network, in Proc. IEEE Power & Energy Society General Meet. (PESGM), Atlanta, GA, USA, 2019, pp. 1–5.
DOI
[74]
L. Chong, J. Rong, D. Wenqiang, S. Weicheng, and M. Xiping, Short-term PV generation forecasting based on weather type clustering and improved GPR model, in Proc. China Int. Conf. Electricity Distribution (CICED), Xi’an, China, 2016, pp. 1–5.
DOI
[75]
Y. K. Wu, C. R. Chen, and H. Abdul Rahman, A novel hybrid model for short-term forecasting in PV power generation, Int. J. Photoenergy, vol. 2014, p. 569249, 2014.
DOI
[76]

W. Van Deventer, E. Jamei, G. S. Thirunavukkarasu, M. Seyedmahmoudian, T. K. Soon, B. Horan, S. Mekhilef, and A. Stojcevski, Short-term PV power forecasting using hybrid GASVM technique, Renew. Energy, vol. 140, pp. 367–379, 2019.

[77]
Y. Wang, B. Feng, Q. S. Hua, and L. Sun, Short-term solar power forecasting: A combined long short-term memory and Gaussian process regression method, Sustainability, vol. 13, no. 7, p. 3665, 2021.
DOI
[78]

Y. K. Semero, J. Zhang, and D. Zheng, PV power forecasting using an integrated GA-PSO-ANFIS approach and Gaussian process regression based feature selection strategy, CSEE J. Power Energy Syst., vol. 4, no. 2, pp. 210–218, 2018.

[79]
S. West, Photovoltaic power forecasting with support vector machines, bachelor thesis, The University of Newcastle, Newcastle, Australia, 2005.
[80]
B. Wang, J. Che, B. Wang, and S. Feng, A solar power prediction using support vector machines based on multi-source data fusion, in Proc. Int. Conf. Power System Technology (POWERCON), Guangzhou, China, 2018, pp. 4573–4577.
[81]
X. Yang, F. Jiang, and H. Liu, Short-Term Solar Radiation Prediction based on SVM with Similar Data, in Proc. 2nd IET Renewable Power Generation Conf. (RPG 2013), Beijing, China, 2013, pp. 1–4.
[82]

F. Wang, Z. Zhen, B. Wang, and Z. Mi, Comparative study on KNN and SVM based weather classification models for day ahead short term solar PV power forecasting, Appl. Sci., vol. 8, no. 1, p. 28, 2017.

[83]
A. Golder, J. Jneid, J. Zhao, and F. Bouffard, Machine learning-based demand and PV power forecasts, in Proc. IEEE Electrical Power and Energy Conf. (EPEC), Montreal, Canada, 2019, pp. 1–6.
DOI
[84]

L. Wang, Y. Liu, T. Li, X. Xie, and C. Chang. The short term forecasting of asymmetry photovoltaic power based on the feature extraction of PV power and SVM algorithm, Symmetry, vol. 12, no. 11, p. 1777, 2020.

[85]
B. Zazoum, Solar photovoltaic power prediction using different machine learning methods, Energy Rep., vol. 8, pp. 19–25, 2022.
DOI
[86]
Z. Zhen, F. Wang, Y. Sun, Z. Mi, C. Liu, B. Wang, and J. Lu, SVM based cloud classification model using total sky images for PV power forecasting, in Proc. IEEE Power & Energy Society Innovative Smart Grid Technologies Conf. (ISGT), Washington, DC, USA, 2015, pp. 1–5.
DOI
[87]
Q. Song, F. Li, J. Qian, J. Zhao, and Z. Chen, Photovoltaic power prediction based on principal component analysis and Support Vector Machine, in Proc. IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), Melbourne, Australia, 2016, pp. 815–820.
[88]
R. Nguyen, Y. Yang, A. Tohmeh, and H. G. Yeh, Predicting PV Power Generation using SVM Regression, in Proc. IEEE Green Energy and Smart Systems Conf. (IGESSC), Long Beach, CA, USA, 2021, pp. 1–5.
DOI
[89]
A. Yona, T. Senjyu, A. Y. Saber, T. Funabashi, H. Sekine, and C. -H. Kim, Application of neural network to 24-hour-ahead generating power forecasting for PV system, in Proc. IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, USA. IEEE, 2008, pp. 1–6.
[90]

M. K. Park, J. M. Lee, W. H. Kang, J. M. Choi, and K. H. Lee, Predictive model for PV power generation using RNN (LSTM), J. Mech. Sci. Technol., vol. 35, no. 2, pp. 795–803, 2021.

[91]

D. Lee and K. Kim, Recurrent neural network-based hourly prediction of photovoltaic power output using meteorological information, Energies, vol. 12, no. 2, p. 215, 2019.

[92]
M. Massaoudi, I. Chihi, L. Sidhom, M. Trabelsi, S. S. Refaat, and F. S. Oueslati, Performance evaluation of deep recurrent neural networks architectures: Application to PV power forecasting, in Proc. 2nd Int. Conf. Smart Grid and Renewable Energy (SGRE), Doha, Qatar, 2019, pp. 1–6.
DOI
[93]
N. Park and H. K. Ahn, Multi-Layer RNN-based Short-term Photovoltaic Power Forecasting using IoT Dataset, in Proc. 2019 AEIT Int. Annual Conf. (AEIT), Florence, Italy, 2019, pp. 1–5.
DOI
[94]

Y. Jung, J. Jung, B. Kim, and S. Han, Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities, J. Clean. Prod., vol. 250, p. 119476, 2020.

[95]

H. K. Ahn and N. Park, Deep RNN-based photovoltaic power short-term forecast using power IoT sensors, Energies, vol. 14, no. 2, p. 436, 2021.

[96]

P. Gupta and R. Singh, PV power forecasting based on data-driven models: A review, Int. J. Sustain. Eng., vol. 14, no. 6, pp. 1733–1755, 2021.

[97]

M. Abdel-Nasser and K. Mahmoud, Accurate photovoltaic power forecasting models using deep LSTM-RNN, Neural Comput. Appl., vol. 31, no. 7, pp. 2727–2740, 2019.

[98]
M. H. Kermia, D. Abbes, and J. Bosche, Photovoltaic power prediction using a recurrent neural network RNN, in Proc. 6th IEEE Int. Energy Conf. (ENERGYCon), Tunis, Tunisia, 2020, pp. 545–549.
DOI
[99]
M. Sabri and M. El Hassouni, A comparative study of LSTM and RNN for photovoltaic power forecasting, in Proc. Int. Conf. Advanced Technologies for Humanity (ICATH'2021), Rabat, Morocco, 2021, pp. 265–274.
DOI
[100]

C. Huang, A. Bensoussan, M. Edesess, and K. L. Tsui, Improvement in artificial neural network-based estimation of grid connected photovoltaic power output, Renew. Energy, vol. 97, pp. 838–848, 2016.

[101]

H. Sheng, J. Xiao, Y. Cheng, Q. Ni, and S. Wang, Short-term solar power forecasting based on weighted Gaussian process regression, IEEE Trans. Ind. Electron., vol. 65, no. 1, pp. 300–308, 2018.

[102]

F. Lubbe, J. Maritz, and T. Harms, Evaluating the potential of Gaussian process regression for solar radiation forecasting: A case study, Energies, vol. 13, no. 20, p. 5509, 2020.

[103]

A. Chaouachi, R. M. Kamel, and K. Nagasaka, Neural network ensemble-based solar power generation short-term forecasting, J. Adv. Comput. Intell. Intell. Inform., vol. 14, no. 1, pp. 69–75, 2010.

[104]
H. Mori and M. Takahashi, A prediction method for photovoltaic power generation with advanced radial basis function network, in Proc. IEEE PES Innovative Smart Grid Technologies, Tianjin, China, 2012, pp. 1–6.
DOI
[105]
W. Y. Chang, Power generation forecasting of solar photovoltaic system using radial basis function neural network, Appl. Mech. Mater., vol. 368–370, pp. 1262–1265, 2013.
DOI
[106]

F. Tian, X. Fan, R. Wang, H. Qin, and Y. Fan, A power forecasting method for ultra-short-term photovoltaic power generation using transformer model, Math. Probl. Eng., vol. 2022, p. 9421400, 2022.

[107]
Q. T. Phan, Y. K. Wu, and Q. D. Phan, An Approach Using Transformer-based Model for Short-term PV generation forecasting, in Proc. 8th Int. Conf. Applied System Innovation (ICASI), Nantou, China, 2022, pp. 17–20.
DOI
[108]
N. Kim, H. Lee, J. Lee, and B. Lee, Transformer based prediction method for solar power generation data, in Proc. Int. Conf. Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea, 2021, pp. 7–9.
DOI
[109]

X. Li, Q. Yang, Z. Lou, and W. Yan, Deep learning based module defect analysis for large-scale photovoltaic farms, IEEE Trans. Energy Convers., vol. 34, no. 1, pp. 520–529, 2019.

[110]
S. D. Lu, M. H. Wang, S. E. Wei, H. D. Liu, and C. -C. Wu, Photovoltaic module fault detection based on a convolutional neural network, Processes, vol. 9, no. 9, p. 1635, 2021.
DOI
[111]

C. Bu, T. Liu, T. Wang, H. Zhang, and S. Sfarra, A CNN-architecture-based photovoltaic cell fault classification method using thermographic images, Energies, vol. 16, no. 9, p. 3749, 2023.

[112]

A. Y. Appiah, X. Zhang, B. B. K. Ayawli, and F. Kyeremeh, Long short-term memory networks based automatic feature extraction for photovoltaic array fault diagnosis, IEEE Access, vol. 7, pp. 30089–30101, 2019.

[113]

V. Veerasamy, N. I. A. Wahab, M. L. Othman, S. Padmanaban, K. Sekar, R. Ramachandran, H. Hizam, A. Vinayagam, and M. Z. Islam, LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system, IEEE Access, vol. 9, pp. 32672–32687, 2021.

[114]

Z. Mustafa, A. S. A. Awad, M. Azzouz, and A. Azab, Fault identification for photovoltaic systems using a multi-output deep learning approach, Expert Syst. Appl., vol. 211, p. 118551, 2023.

[115]

Z. Chen, F. Han, L. Wu, J. Yu, S. Cheng, P. Lin, and H. Chen, Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents, Energy Convers. Manag., vol. 178, pp. 250–264, 2018.

[116]
Y. Liu, B. Yan, D. Qian, and F. Liu, Research on fault diagnosis of photovoltaic array based on random forest algorithm, in Proc. IEEE Int. Conf. Power Electronics, Computer Applications (ICPECA), Shenyang, China, 2021, pp. 194–198.
[117]
S. Gong, X. Wu, and Z. Zhang, Fault diagnosis method of photovoltaic array based on random forest algorithm, in Proc. 2020 39th Chinese Control Conf. (CCC), Shenyang, China, 2020, pp. 4249–4254.
DOI
[118]

M. Louzazni and E. Aroudam, An intelligent fault diagnosis method based on neural networks for photovoltaic system, Int. J. Mechatron., Electr. Comput. Technol., vol. 4, no. 4, pp. 602–609, 2014.

[119]

S. Voutsinas, D. Karolidis, I. Voyiatzis, and M. Samarakou, Development of a multi-output feed-forward neural network for fault detection in photovoltaic systems, Energy Rep., vol. 8, pp. 33–42, 2022.

[120]

R. Hariharan, M. Chakkarapani, G. Saravana Ilango, and C. Nagamani, A method to detect photovoltaic array faults and partial shading in PV systems, IEEE J. Photovoltaics, vol. 6, no. 5, pp. 1278–1285, 2016.

[121]

J. Huang, K. Zeng, Z. Zhang, and W. Zhong, Solar panel defect detection design based on YOLO v5 algorithm, Heliyon, vol. 9, no. 8, pp. e18826, 2023.

[122]

Y. Wang, L. Shen, M. Li, Q. Sun, and X. Li, PV-YOLO: Lightweight YOLO for photovoltaic panel fault detection, IEEE Access, vol. 11, pp. 10966–10976, 2023.

[123]
Z. Cong, H. Sun, S. Wang, B. Li, W. Wang, and K. Zhao, YOLOv5-CPP: Improved YOLOv5-based defect detection for photovoltaic panels, in Proc. 2023 42nd Chinese Control Conf. (CCC), Tianjin, China, 2023, pp. 8294–8299.
DOI
[124]
H. Acikgoz, An automatic detection model for cracks in photovoltaic cells based on electroluminescence imaging using improved YOLOv7, Signal Image Video Process., vol. 18, no. 1, pp. 625–635, 2024.
DOI
[125]

A. Hichri, M. Hajji, M. Mansouri, K. Abodayeh, K. Bouzrara, H. Nounou, and M. Nounou, Genetic-algorithm-based neural network for fault detection and diagnosis: Application to grid-connected photovoltaic systems, Sustainability, vol. 14, no. 17, p. 10518, 2022.

[126]
M. Zhang, H. Zhang, and Y. Gui, Research on photovoltaic composite fault based on SSA-LSTM, in Proc. 5th Int. Conf. Intelligent Control, Measurement and Signal Processing (ICMSP), Chengdu, China, 2023, pp. 336–339.
DOI
[127]
A. Mellit and S. Boubaker, An effective ensemble learning method for fault diagnosis of photovoltaic arrays, in Proc. 3rd Int. Conf. Electronic Engineering and Renewable Energy Systems, Saidia, Morocco, 2022, pp. 687–695.
DOI
[128]
X. Cai and R. J. Wai, Intelligent DC arc-fault detection of solar PV power generation system via optimized VMD-based signal processing and PSO–SVM classifier, IEEE J. Photovoltaics, vol. 12, no. 4, pp. 1058–1077, 2022.
DOI
[129]
W. Rezgui, L. H. Mouss, N. K. Mouss, M. D. Mouss, and M. Benbouzid, A smart algorithm for the diagnosis of short-circuit faults in a photovoltaic generator, in Proc. 1st Int. Conf. Green Energy (ICGE 2014), Sfax, Tunisia, 2014, pp. 139–143.
DOI
[130]

G. S. Eldeghady, H. A. Kamal, and M. A. M. Hassan, Fault diagnosis for PV system using a deep learning optimized via PSO heuristic combination technique, Electr. Eng., vol. 105, no. 4, pp. 2287–2301, 2023.

[131]
K. Attouri, M. Hajji, M. Mansouri, M. -F. Harkat, A. Kouadri, H. Nounou, and M. Nounou, Fault detection in photovoltaic systems using machine learning technique, in Proc. 17th Int. Multi-Conf. Systems, Signals & Devices (SSD), Monastir, Tunisia. IEEE, 2020, pp. 207–212.
DOI
[132]
R. K. Mandal, N. Anand, N. Sahu, and P. Kale, PV System Fault Classification using SVM Accelerated by Dimension Reduction using PCA, in Proc. IEEE 9th Power India Int. Conf. (PIICON), Sonepat, India, 2020, pp. 1–16.
DOI
[133]

H. Belmili, S. M. Ait Cheikh, M. Haddadi, and C. Larbes, Design and development of a data acquisition system for photovoltaic modules characterization, Renew. Energy, vol. 35, no. 7, pp. 1484–1492, 2010.

[134]

M. Fan, V. Vittal, G. T. Heydt, and R. Ayyanar, Preprocessing uncertain photovoltaic data, IEEE Trans. Sustain. Energy, vol. 5, no. 1, pp. 351–352, 2014.

[135]

M. G. De Giorgi, P. M. Congedo, and M. Malvoni, Photovoltaic power forecasting using statistical methods: Impact of weather data, IET Sci. Meas. Technol., vol. 8, no. 3, pp. 90–97, 2014.

[136]

M. Malvoni, M. G. De Giorgi, and P. M. Congedo, Data on Support Vector Machines (SVM) model to forecast photovoltaic power, Data Brief, vol. 9, pp. 13–16, 2016.

[137]

B. Wolff, J. Kühnert, E. Lorenz, O. Kramer, and D. Heinemann, Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data, Sol. Energy, vol. 135, pp. 197–208, 2016.

[138]
S. V. Oprea and A. Bâra, Ultra-short-term forecasting for photovoltaic power plants and real-time key performance indicators analysis with big data solutions. Two case studies - PV Agigea and PV Giurgiu located in Romania, Comput. Ind., vol. 120, p. 103230, 2020.
DOI
[139]

L. Ge, T. Du, C. Li, Y. Li, J. Yan, and M. Rafiq, Virtual collection for distributed photovoltaic data: Challenges, methodologies, and applications, Energies, vol. 15, no. 23, p. 8783, 2022.

[140]
M. Park and I. -K. Yu, A novel real-time simulation technique of photovoltaic generation systems using RTDS, IEEE Trans. Energy Convers., vol. 19, no. 1, pp. 164–169, 2004.
DOI
[141]

M. H. Ali, A. Rabhi, A. El Hajjaji, and G. M. Tina, Real time fault detection in photovoltaic systems, Energy Procedia, vol. 111, pp. 914–923, 2017.

[142]

A. K. Rohit, A. Tomar, A. Kumar, and S. Rangnekar, Virtual lab based real-time data acquisition, measurement and monitoring platform for solar photovoltaic module, Resour. Effic. Technol., vol. 3, no. 4, pp. 446–451, 2017.

[143]

M. Cubukcu and A. Akanalci, Real-time inspection and determination methods of faults on photovoltaic power systems by thermal imaging in Turkey, Renew. Energy, vol. 147, pp. 1231–1238, 2020.

[144]
I. Allafi and T. Iqbal, Design and implementation of a low cost web server using ESP32 for real-time photovoltaic system monitoring, in Proc. IEEE Electrical Power and Energy Conf. (EPEC), Saskatoon, Canada, 2017, pp. 1–5.
DOI
[145]

I. M. Moreno-Garcia, E. J. Palacios-Garcia, V. Pallares-Lopez, I. Santiago, M. J. Gonzalez-Redondo, M. Varo-Martinez, and R. J. Real-Calvo, Real-time monitoring system for a utility-scale photovoltaic power plant, Sensors, vol. 16, no. 6, p. 770, 2016.

[146]

S. Samara and E. Natsheh, Intelligent real-time photovoltaic panel monitoring system using artificial neural networks, IEEE Access, vol. 7, pp. 50287–50299, 2019.

[147]

M. K. Behera, I. Majumder, and N. Nayak, Solar photovoltaic power forecasting using optimized modified extreme learning machine technique, Eng. Sci. Technol. Int. J., vol. 21, no. 3, pp. 428–438, 2018.

[148]

K. Wang, X. Qi, and H. Liu, A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network, Appl. Energy, vol. 251, p. 113315, 2019.

[149]

L. Liang, T. Su, Y. Gao, F. Qin, and M. Pan, FCDT-IWBOA-LSSVR: An innovative hybrid machine learning approach for efficient prediction of short-to-mid-term photovoltaic generation, J. Clean. Prod., vol. 385, p. 135716, 2023.

[150]

A. Mellit and S. Kalogirou, Artificial intelligence and Internet of Things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions, Renew. Sustain. Energy Rev., vol. 143, p. 110889, 2021.

[151]

Z. Yahya, S. Imane, H. Hicham, A. Ghassane, and E. Bouchini-Idrissi Safia, Applied imagery pattern recognition for photovoltaic modules’ inspection: A review on methods, challenges and future development, Sustain. Energy Technol. Assess., vol. 52, p. 102071, 2022.

[152]

Q. Chen, X. Li, Z. Zhang, C. Zhou, Z. Guo, Z. Liu, and H. Zhang, Remote sensing of photovoltaic scenarios: Techniques, applications and future directions, Appl. Energy, vol. 333, p. 120579, 2023.

[153]

Y. Tang, K. Yang, S. Zhang, and Z. Zhang, Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy, Renew. Sustain. Energy Rev., vol. 162, p. 112473, 2022.

[154]

J. Schreiber and B. Sick, Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts, Energy AI, vol. 14, p. 100249, 2023.

[155]
L. Liu, D. Wang, J. Li, and S. Wang, An efficient hot spot detection method with small sample learning for photovoltaic panels, in Proc. 6th Int. Conf. Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, 2023, pp. 673–678.
DOI
[156]
J. Zhou and X. Luo, A small sample photovoltaic hot spot identification method based on deep transfer learning, J. Phys.: Conf. Ser., vol. 2467, no. 1, p. 012009, 2023.
DOI
[157]
X. Chang, W. Li, J. Ma, T. Yang, and A. Y. Zomaya, Interpretable machine learning in sustainable edge computing: A case study of short-term photovoltaic power output prediction, in Proc. ICASSP 2020 - 2020 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 8981–8985.
DOI
[158]

X. Chang, W. Li, and A. Y. Zomaya, A lightweight short-term photovoltaic power prediction for edge computing, IEEE Trans. Green Commun. Netw., vol. 4, no. 4, pp. 946–955, 2020.

[159]

W. Tang, Q. Yang, X. Hu, and W. Yan, Deep learning-based linear defects detection system for large-scale photovoltaic plants based on an edge-cloud computing infrastructure, Sol. Energy, vol. 231, pp. 527–535, 2022.

Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 31 October 2023
Revised: 29 December 2023
Accepted: 19 January 2024
Published: 08 May 2024
Issue date: December 2024

Copyright

© The author(s) 2024.

Acknowledgements

Acknowledgment

The research was supported by the National Key R&D Program of China (No. 2022YFE0196100), and Fundamental Research Grant Scheme (FRGS) of Malaysia (FRGS/1/2022/TK0/UTAR/02/8). The above two grants are also parked under China-Malaysia Intergovernmental Science, Technology and Innovation Cooperative Programme 2023 (offer letter MOSTI.700-2/2/8(39), dated 9 February 2023).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return