Machine learning predictions for fault detections in solar photovoltaic system: A bibliographic outlook
Vol 9, Issue 2, 2025
Abstract
Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.
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Abd el-Ghany, H. A., ELGebaly, A. E., & Taha, I. B. M. (2021). A new monitoring technique for fault detection and classification in PV systems based on rate of change of voltage-current trajectory. International Journal of Electrical Power & Energy Systems, 133, 107248. https://doi.org/10.1016/j.ijepes.2021.107248
Ağbulut, Ü., Gürel, A. E., & Biçen, Y. (2021). Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison. Renewable and Sustainable Energy Reviews, 135, 110114. https://doi.org/10.1016/j.rser.2020.110114
Aghaei, M., Kolahi, M., Nedaei, A., et al. (2023). A Holistic Study on Failures and Diagnosis Techniques in Photovoltaic Modules, Components and Systems. In: Proceedings of 2023 International Conference on Future Energy Solutions (FES). pp. 1–6.
Ali, M. U., Khan, H. F., Masud, M., et al. (2020). A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography. Solar Energy, 208, 643–651. https://doi.org/10.1016/j.solener.2020.08.027
Almeida, M. P., Perpiñán, O., & Narvarte, L. (2015). PV power forecast using a nonparametric PV model. Solar Energy, 115, 354–368. https://doi.org/10.1016/j.solener.2015.03.006
Alrahim Shannan, N. M. A., Yahaya, N. Z., & Singh, B. (2013). Single-diode model and two-diode model of PV modules: A comparison. 2013 IEEE International Conference on Control System, Computing and Engineering; 29 November 2013–1 December 2013; Penang, Malaysia. pp. 210–214.
Alzahrani, A., Kimball, J. W., & Dagli, C. (2014). Predicting Solar Irradiance Using Time Series Neural Networks. Procedia Computer Science, 36, 623–628. https://doi.org/10.1016/j.procs.2014.09.065
Amrouche, B., & Le Pivert, X. (2014). Artificial neural network based daily local forecasting for global solar radiation. Applied Energy, 130, 333–341. https://doi.org/10.1016/j.apenergy.2014.05.055
Antonanzas, J., Urraca, R., Pernía-Espinoza, A., et al. (2017). Single and Blended Models for Day-Ahead Photovoltaic Power Forecasting. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (editors). Hybrid Artificial Intelligent Systems. Springer, Cham.
Apeh, D. Oliver. O., & Nwulu, P. N. (2024). The Food‑Energy‑Water Nexus Optimization: A Systematic Literature Review. Research on World Agricultural Economy, 247–269. https://doi.org/10.36956/rwae.v5i4.1170
Apeh, O. O., & Nwulu, N. I. (2024). The water-energy-food-ecosystem nexus scenario in Africa: Perspective and policy implementations. Energy Reports, 11, 5947–5962. https://doi.org/10.1016/j.egyr.2024.05.060
Apeh, O. O., & Nwulu, N. I. (2024). Unlocking economic growth: Harnessing renewable energy to mitigate load shedding in Southern Africa. E-Prime—Advances in Electrical Engineering, Electronics and Energy, 10, 100869. https://doi.org/10.1016/j.prime.2024.100869
Apeh, O. O., & Nwulu, N. I. (2025). Improving traceability and sustainability in the agri-food industry through blockchain technology: A bibliometric approach, benefits and challenges. Energy Nexus, 17, 100388. https://doi.org/10.1016/j.nexus.2025.100388
Apeh, O. O., Meyer, E. L., & Overen, O. K. (2021). Modeling and experimental analysis of battery charge controllers for comparing three off-grid photovoltaic power plants. Heliyon, 7(11), e08331. https://doi.org/10.1016/j.heliyon.2021.e08331
Apeh, O. O., Meyer, E. L., & Overen, O. K. (2022). Contributions of Solar Photovoltaic Systems to Environmental and Socioeconomic Aspects of National Development—A Review. Energies, 15(16), 5963. https://doi.org/10.3390/en15165963
Apeh, O. O., Overen, O. K., & Meyer, E. L. (2021). Monthly, Seasonal and Yearly Assessments of Global Solar Radiation, Clearness Index and Diffuse Fractions in Alice, South Africa. Sustainability, 13(4), 2135. https://doi.org/10.3390/su13042135
Ashrae, A. V. (2020). Producing Net Zero Energy Buildings, 2008. Whole Building Design Guide.
Asrari, A., Wu, T. X., & Ramos, B. (2017). A Hybrid Algorithm for Short-Term Solar Power Prediction—Sunshine State Case Study. IEEE Transactions on Sustainable Energy, 8(2), 582–591. https://doi.org/10.1109/tste.2016.2613962
Babu, B. C., & Gurjar, S. (2014). A Novel Simplified Two-Diode Model of Photovoltaic (PV) Module. IEEE Journal of Photovoltaics, 4(4), 1156–1161. https://doi.org/10.1109/jphotov.2014.2316371
Badrudeen, T. U., Nwulu, N. I., & Gbadamosi, S. L. (2023). Neural Network Based Approach for Steady-State Stability Assessment of Power Systems. Sustainability, 15(2), 1667. https://doi.org/10.3390/su15021667
Bakay, M. S., & Ağbulut, Ü. (2021). Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms. Journal of Cleaner Production, 285, 125324. https://doi.org/10.1016/j.jclepro.2020.125324
Basaran, K., Özçift, A., & Kılınç, D. (2019). A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm. Arabian Journal for Science and Engineering, 44(8), 7159–7171. https://doi.org/10.1007/s13369-019-03841-7
Basnet, B., Chun, H., & Bang, J. (2020). An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems. Journal of Sensors, 2020, 1–11. https://doi.org/10.1155/2020/6960328
Cabeza, R. T., & Potts, A. S. (2021). Fault diagnosis and isolation based on Neuro-Fuzzy models applied to a photovoltaic system. IFAC-PapersOnLine, 54(14), 358–363. https://doi.org/10.1016/j.ifacol.2021.10.380
Cervone, G., Clemente-Harding, L., Alessandrini, S., et al. (2017). Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble. Renewable Energy, 108, 274–286. https://doi.org/10.1016/j.renene.2017.02.052
Chaouachi, A., Kamel, R. M., & Nagasaka, K. (2010). A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system. Solar Energy, 84(12), 2219–2229. https://doi.org/10.1016/j.solener.2010.08.004
Chennoufi, K., Ferfra, M., & Mokhlis, M. (2021). An accurate modelling of Photovoltaic modules based on two-diode model. Renewable Energy, 167, 294–305. https://doi.org/10.1016/j.renene.2020.11.085
Chérifa, K. M. K., Badia, A., Abou Soufiane, B. (2020). New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems. Energy, 211, 118591. https://doi.org/10.1016/j.energy.2020.118591
Cristaldi, L., Leone, G., & Ottoboni, R. (2017). A hybrid approach for solar radiation and photovoltaic power short-term forecast. In: Proceedingd of 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC); 22–25 May 2017; Turin, Italy. pp. 1–6.
Cuce, E., Cuce, P. M., & Bali, T. (2013). An experimental analysis of illumination intensity and temperature dependency of photovoltaic cell parameters. Applied Energy, 111, 374–382. https://doi.org/10.1016/j.apenergy.2013.05.025
Cuce, E., Cuce, P. M., Karakas, I. H., et al. (2017). An accurate model for photovoltaic (PV) modules to determine electrical characteristics and thermodynamic performance parameters. Energy Conversion and Management, 146, 205–216. https://doi.org/10.1016/j.enconman.2017.05.022
David, L. O., Nwulu, N. I., Aigbavboa, C. O., et al. (2022). Integrating fourth industrial revolution (4IR) technologies into the water, energy & food nexus for sustainable security: A bibliometric analysis. Journal of Cleaner Production, 363, 132522. https://doi.org/10.1016/j.jclepro.2022.132522
De Leone, R., Pietrini, M., & Giovannelli, A. (2015). Photovoltaic energy production forecast using support vector regression. Neural Computing and Applications, 26(8), 1955–1962. https://doi.org/10.1007/s00521-015-1842-y
Dong, Z., Yang, D., Reindl, T., et al. (2013). Short-term solar irradiance forecasting using exponential smoothing state space model. Energy, 55, 1104–1113. https://doi.org/10.1016/j.energy.2013.04.027
Eskandari, A., Milimonfared, J., & Aghaei, M. (2020). Line-line fault detection and classification for photovoltaic systems using ensemble learning model based on I-V characteristics. Solar Energy, 211, 354–365. https://doi.org/10.1016/j.solener.2020.09.071
Fan, J., Wang, X., Wu, L., et al. (2018). Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Conversion and Management, 164, 102–111. https://doi.org/10.1016/j.enconman.2018.02.087
Fan, J., Wu, L., Zhang, F., et al. (2019). Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China. Renewable and Sustainable Energy Reviews, 105, 168–186. https://doi.org/10.1016/j.rser.2019.01.040
Garoudja, E., Chouder, A., Kara, K., et al. (2017). An enhanced machine learning based approach for failures detection and diagnosis of PV systems. Energy Conversion and Management, 151, 496–513. https://doi.org/10.1016/j.enconman.2017.09.019
Gholami, A., Ameri, M., Zandi, M., et al. (2021). A single-diode model for photovoltaic panels in variable environmental conditions: Investigating dust impacts with experimental evaluation. Sustainable Energy Technologies and Assessments, 47, 101392. https://doi.org/10.1016/j.seta.2021.101392
Gholami, A., Ameri, M., Zandi, M., et al. (2022). Electrical, thermal and optical modeling of photovoltaic systems: Step-by-step guide and comparative review study. Sustainable Energy Technologies and Assessments, 49, 101711. https://doi.org/10.1016/j.seta.2021.101711
Gholami, A., Ameri, M., Zandi, M., et al. (2022). Predicting solar photovoltaic electrical output under variable environmental conditions: Modified semi-empirical correlations for dust. Energy for Sustainable Development, 71, 389–405. https://doi.org/10.1016/j.esd.2022.10.012
Gholami, A., Ameri, M., Zandi, M., et al. (2022). Step-By-Step Guide to Model Photovoltaic Panels: An Up-To-Date Comparative Review Study. IEEE Journal of Photovoltaics, 12(4), 915–928. https://doi.org/10.1109/jphotov.2022.3169525
Gholami, A., Ameri, M., Zandi, M., et al. (2023). Impact of harsh weather conditions on solar photovoltaic cell temperature: Experimental analysis and thermal-optical modeling. Solar Energy, 252, 176–194. https://doi.org/10.1016/j.solener.2023.01.039
Gopi, A., Sharma, P., Sudhakar, K., et al. (2022). Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques. Sustainability, 15(1), 439. https://doi.org/10.3390/su15010439
Hajizadeh, Y. (2019). Machine learning in oil and gas; a SWOT analysis approach. Journal of Petroleum Science and Engineering, 176, 661–663. https://doi.org/10.1016/j.petrol.2019.01.113
Hajji, M., Harkat, M.-F., Kouadri, A., et al. (2021). Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems. European Journal of Control, 59, 313–321. https://doi.org/10.1016/j.ejcon.2020.03.004
Harrou, F., Saidi, A., Sun, Y., et al. (2021). Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes. IEEE Journal of Photovoltaics, 11(3), 806–818. https://doi.org/10.1109/jphotov.2021.3057169
He, C., Liu, J., Xu, F., et al. (2020). Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods. Energy Conversion and Management, 220, 113111. https://doi.org/10.1016/j.enconman.2020.113111
Hussain, S., & AlAlili, A. (2017). A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks. Applied Energy, 208, 540–550. https://doi.org/10.1016/j.apenergy.2017.09.100
Ibrahim, I. A., & Khatib, T. (2017). A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm. Energy Conversion and Management, 138, 413–425. https://doi.org/10.1016/j.enconman.2017.02.006
Ismaen, R., Kucukvar, M., El Mekkawy, T. Y., et al. (2023). Optimization and enviro-economic assessment of solar-cooling systems towards sustainable development: A case study of Qatar. Journal of Cleaner Production, 419, 138253. https://doi.org/10.1016/j.jclepro.2023.138253
İzgi, E., Öztopal, A., Yerli, B., et al. (2012). Short–mid-term solar power prediction by using artificial neural networks. Solar Energy, 86(2), 725–733. https://doi.org/10.1016/j.solener.2011.11.013
Jäger-Waldau, A. (2022). Snapshot of photovoltaics—February 2022. EPJ Photovoltaics, 13, 9. https://doi.org/10.1051/epjpv/2022010
Jain, S., & Jain, P. K. (2017). The rise of Renewable Energy implementation in South Africa. Energy Procedia, 143, 721–726. https://doi.org/10.1016/j.egypro.2017.12.752
Jang, H. S., Bae, K. Y., Park, H.-S., et al. (2016). Solar Power Prediction Based on Satellite Images and Support Vector Machine. IEEE Transactions on Sustainable Energy, 7(3), 1255–1263. https://doi.org/10.1109/tste.2016.2535466
Jia, D., Yang, L., Lv, T., et al. (2022). Evaluation of machine learning models for predicting daily global and diffuse solar radiation under different weather/pollution conditions. Renewable Energy, 187, 896–906. https://doi.org/10.1016/j.renene.2022.02.002
Joshua, S. R., Yeon, A. N., Park, S., et al. (2024). A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting. Applied Sciences, 14(18), 8573. https://doi.org/10.3390/app14188573
Jović, S., Aničić, O., Marsenić, M., et al. (2016). Solar radiation analyzing by neuro-fuzzy approach. Energy and Buildings, 129, 261–263. https://doi.org/10.1016/j.enbuild.2016.08.020
Jyothy, L. P. N., Sindhu, M. R. (2018). An artificial neural network based MPPT algorithm for solar PV system. In: Proceedings of 2018 4th International Conference on Electrical Energy Systems (ICEES); 7–9 February 2018; Chennai, India. pp. 375–380.
Kaba, K., Sarıgül, M., Avcı, M., et al. (2018). Estimation of daily global solar radiation using deep learning model. Energy, 162, 126–135. https://doi.org/10.1016/j.energy.2018.07.202
Kapucu, C., & Cubukcu, M. (2021). A supervised ensemble learning method for fault diagnosis in photovoltaic strings. Energy, 227, 120463. https://doi.org/10.1016/j.energy.2021.120463
Kara Mostefa Khelil, C., Amrouche, B., Kara, K., et al. (2021). The impact of the ANN’s choice on PV systems diagnosis quality. Energy Conversion and Management, 240, 114278. https://doi.org/10.1016/j.enconman.2021.114278
Karamizadeh, S., Abdullah, S. M., Halimi, M., et al. (2014). Advantage and drawback of support vector machine functionality. In: Proceedings of 2014 International Conference on Computer, Communications, and Control Technology (I4CT); 2–4 September 2014; Langkawi, Malaysia. pp. 63–65.
Khalilov, D. A., Jumaboyeva, N. A. K., Kurbonova, T. M. K. (2021). Advantages and Applications of Neural Networks. Acadamic Research of Education Science, 2(2), 1153–1159.
Khandakar, A., E. H. Chowdhury, M., Khoda Kazi, M.-, Benhmed, K., et al. (2019). Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar. Energies, 12(14), 2782. https://doi.org/10.3390/en12142782
Kim, J., Kim, D., Yoo, W., et al. (2017). Daily prediction of solar power generation based on weather forecast information in Korea. IET Renewable Power Generation, 11(10), 1268–1273. Portico. https://doi.org/10.1049/iet-rpg.2016.0698
Kwon, Y., Kwasinski, A., & Kwasinski, A. (2019). Solar Irradiance Forecast Using Naïve Bayes Classifier Based on Publicly Available Weather Forecasting Variables. Energies, 12(8), 1529. https://doi.org/10.3390/en12081529
Lazzaretti, A. E., Costa, C. H. da, Rodrigues, M. P., Yamada, G. D., et al. (2020). A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants. Sensors, 20(17), 4688. https://doi.org/10.3390/s20174688
Li, H., Liu, Z., Liu, K., et al. (2017). Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening. International Journal of Photoenergy, 2017, 1–10. https://doi.org/10.1155/2017/4194251
Li, H., Zhang, Z., & Liu, Z. (2017). Application of Artificial Neural Networks for Catalysis: A Review. Catalysts, 7(10), 306. https://doi.org/10.3390/catal7100306
Li, H., Zhang, Z., & Zhao, Z.-Z. (2019). Data-Mining for Processes in Chemistry, Materials, and Engineering. Processes, 7(3), 151. https://doi.org/10.3390/pr7030151
Lin, P., Qian, Z., Lu, X., et al. (2022). Compound fault diagnosis model for Photovoltaic array using multi-scale SE-ResNet. Sustainable Energy Technologies and Assessments, 50, 101785. https://doi.org/10.1016/j.seta.2021.101785
Liu, Z., Li, H., Tang, X., et al. (2016). Extreme learning machine: a new alternative for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters. SpringerPlus, 5(1). https://doi.org/10.1186/s40064-016-2242-1
Liu, Z., Liu, K., Li, H., et al. (2015). Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters. PLOS ONE, 10(12), e0143624. https://doi.org/10.1371/journal.pone.0143624
Luo, X., Xia, J., & Liu, Y. (2021). Extraction of dynamic operation strategy for standalone solar-based multi-energy systems: A method based on decision tree algorithm. Sustainable Cities and Society, 70, 102917. https://doi.org/10.1016/j.scs.2021.102917
Malik, A., Haque, A., Satya Bharath, K. V., Jaffery, Z. A. (2021). Transfer Learning-Based Novel Fault Classification Technique for Grid-Connected PV Inverter. In: Mekhilef, S., Favorskaya, M., Pandey, R. K., Shaw, R. N. (editors). Innovations in Electrical and Electronic Engineering. Springer. pp. 217–224.
Maluta, E. N., Mulaudzi, S. T. (2018). Evaluation of the temperature based models for the estimation of global solar radiation in Pretoria, Gauteng province of South Africa. International Energy Journal, 18(2).
Mandal, P., Madhira, S. T. S., haque, A. U., et al. (2012). Forecasting Power Output of Solar Photovoltaic System Using Wavelet Transform and Artificial Intelligence Techniques. Procedia Computer Science, 12, 332–337. https://doi.org/10.1016/j.procs.2012.09.080
Martín, L., Zarzalejo, L. F., Polo, J., et al. (2010). Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning. Solar Energy, 84(10), 1772–1781. https://doi.org/10.1016/j.solener.2010.07.002
Mellit, A., & Kalogirou, S. (2022). Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems. Renewable Energy, 184, 1074–1090. https://doi.org/10.1016/j.renene.2021.11.125
Mellit, A., Pavan, A. M., & Benghanem, M. (2012). Least squares support vector machine for short-term prediction of meteorological time series. Theoretical and Applied Climatology, 111(1–2), 297–307. https://doi.org/10.1007/s00704-012-0661-7
Mellit, A., Tina, G. M., & Kalogirou, S. A. (2018). Fault detection and diagnosis methods for photovoltaic systems: A review. Renewable and Sustainable Energy Reviews, 91, 1–17. https://doi.org/10.1016/j.rser.2018.03.062
Merza, B. N., Wadday, A. G., & Abdullah, A. K. (2024). Study and analysis of fault detection in solar array system based on internet of things. AIP Conference Proceedings, 3092(1).
Meyer, E. L., Apeh, O. O., & Overen, O. K. (2020). Electrical and Meteorological Data Acquisition System of a Commercial and Domestic Microgrid for Monitoring PV Parameters. Applied Sciences, 10(24), 9092. https://doi.org/10.3390/app10249092
Mocanu, E., Nguyen, P. H., & Gibescu, M. (2018). Deep Learning for Power System Data Analysis. Big Data Application in Power Systems, 125–158. https://doi.org/10.1016/b978-0-12-811968-6.00007-3
Mohamed, A., Hamdi, M. S., & Tahar, S. (2015). A Machine Learning Approach for Big Data in Oil and Gas Pipelines. In: Proceedings of 2015 3rd International Conference on Future Internet of Things and Cloud; 24–26 August 2015; Rome, Italy. pp. 585–590.
Muhammad Ehsan, R., Simon, S. P., & Venkateswaran, P. R. (2016). Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron. Neural Computing and Applications, 28(12), 3981–3992. https://doi.org/10.1007/s00521-016-2310-z
Mustafa, Z., Awad, A. S. A., Azzouz, M., et al. (2023). Fault identification for photovoltaic systems using a multi-output deep learning approach. Expert Systems with Applications, 211, 118551. https://doi.org/10.1016/j.eswa.2022.118551
Naderi, S., Banifateme, M., Pourali, O., et al. (2020). Accurate capacity factor calculation of waste-to-energy power plants based on availability analysis and design/off-design performance. Journal of Cleaner Production, 275, 123167. https://doi.org/10.1016/j.jclepro.2020.123167
Nižetić, S., Jurčević, M., Čoko, D., Arıcı, M. (2021a). A novel and effective passive cooling strategy for photovoltaic panel. Renewable and Sustainable Energy Reviews, 145, 111164. https://doi.org/10.1016/j.rser.2021.111164
Nižetić, S., Jurčević, M., Čoko, D., et al. (2021b). Implementation of phase change materials for thermal regulation of photovoltaic thermal systems: Comprehensive analysis of design approaches. Energy, 228, 120546. https://doi.org/10.1016/j.energy.2021.120546
Odabaşı, Ç., & Yıldırım, R. (2020). Machine learning analysis on stability of perovskite solar cells. Solar Energy Materials and Solar Cells, 205, 110284. https://doi.org/10.1016/j.solmat.2019.110284
Olatomiwa, L., Mekhilef, S., Shamshirband, S., et al. (2015). A support vector machine–firefly algorithm-based model for global solar radiation prediction. Solar Energy, 115, 632–644. https://doi.org/10.1016/j.solener.2015.03.015
Olatomiwa, L., Mekhilef, S., Shamshirband, S., et al. (2015). Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria. Renewable and Sustainable Energy Reviews, 51, 1784–1791. https://doi.org/10.1016/j.rser.2015.05.068
Olorunfemi, B. O., Nwulu, N. I., & Ogbolumani, O. A. (2023). Solar panel surface dirt detection and removal based on arduino color recognition. MethodsX, 10, 101967. https://doi.org/10.1016/j.mex.2022.101967
Pandiyan, P., Sitharthan, R., Saravanan, S., et al. (2022). A comprehensive review of the prospects for rural electrification using stand-alone and hybrid energy technologies. Sustainable Energy Technologies and Assessments, 52, 102155. https://doi.org/10.1016/j.seta.2022.102155
Persson, C., Bacher, P., Shiga, T., et al. (2017). Multi-site solar power forecasting using gradient boosted regression trees. Solar Energy, 150, 423–436. https://doi.org/10.1016/j.solener.2017.04.066
Prasad, R., Ali, M., Kwan, P., et al. (2019). Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation. Applied Energy, 236, 778–792. https://doi.org/10.1016/j.apenergy.2018.12.034
Pyloudi, E., Papantoniou, S., & Kolokotsa, D. (2014). Retrofitting an office building towards a net zero energy building. Advances in Building Energy Research, 9(1), 20–33. https://doi.org/10.1080/17512549.2014.917985
Quej, V. H., Almorox, J., Arnaldo, J. A., et al. (2017). ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. Journal of Atmospheric and Solar-Terrestrial Physics, 155, 62–70. https://doi.org/10.1016/j.jastp.2017.02.002
Ramli, M. A. M., Twaha, S., & Al-Turki, Y. A. (2015). Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: Saudi Arabia case study. Energy Conversion and Management, 105, 442–452. https://doi.org/10.1016/j.enconman.2015.07.083
Ramsami, P., & Oree, V. (2015). A hybrid method for forecasting the energy output of photovoltaic systems. Energy Conversion and Management, 95, 406–413. https://doi.org/10.1016/j.enconman.2015.02.052
Ranganai, E., & Sigauke, C. (2020). Capturing Long-Range Dependence and Harmonic Phenomena in 24-Hour Solar Irradiance Forecasting: A Quantile Regression Robustification via Forecasts Combination Approach. IEEE Access, 8, 172204–172218. https://doi.org/10.1109/access.2020.3024661
Rashidi, S., Esfahani, J. A., & Hosseinirad, E. (2021). Assessment of solar chimney combined with phase change materials. Journal of the Taiwan Institute of Chemical Engineers, 124, 341–350. https://doi.org/10.1016/j.jtice.2021.03.001
Rezgui, W., Mouss, L.-H., Mouss, N. K., et al. (2014). A smart algorithm for the diagnosis of short-circuit faults in a photovoltaic generator. In: Proceedings of 2014 First International Conference on Green Energy (ICGE 2014); 25–27 March 2014; Sfax, Tunisia. pp. 139–143.
Sabri, N., Tlemcani, A., & Chouder, A. (2018). Intelligent fault supervisory system applied on stand-alone photovoltaic system. In: Proceedings of 2018 International Conference on Applied Smart Systems (ICASS); 24–25 November 2018; Medea, Algeria. pp. 1–5.
Salcedo-Sanz, S., Deo, R. C., Cornejo-Bueno, L., et al. (2018). An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia. Applied Energy, 209, 79–94. https://doi.org/10.1016/j.apenergy.2017.10.076
Sangeetha, M., Manigandan, S., Ashok, B., et al. (2021). Experimental investigation of nanofluid based photovoltaic thermal (PV/T) system for superior electrical efficiency and hydrogen production. Fuel, 286, 119422. https://doi.org/10.1016/j.fuel.2020.119422
Sareh, D., Rahim, Z., & Omid Noudeh, F. (2022). Evaluation of the concentration of suspended particles in underground subway stations in Tehran and its comparison with ambient concentrations. Annals of Environmental Science and Toxicology, 6(1), 019–025. https://doi.org/10.17352/aest.000048
Savaresi, A. (2013). Just Another Climate Conference. Journal of Environmental Law and Policy, 43, 284.
Shahverdian, M. H., Sohani, A., Sayyaadi, H., et al. (2021). A dynamic multi-objective optimization procedure for water cooling of a photovoltaic module. Sustainable Energy Technologies and Assessments, 45, 101111. https://doi.org/10.1016/j.seta.2021.101111
Sharma, A., & Kakkar, A. (2018). Forecasting daily global solar irradiance generation using machine learning. Renewable and Sustainable Energy Reviews, 82, 2254–2269. https://doi.org/10.1016/j.rser.2017.08.066
Sharma, N., Sharma, P., Irwin, D., et al. (2011). Predicting solar generation from weather forecasts using machine learning. In: Proceedings of 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm); 17–20 October 2011; Brussels, Belgium. pp. 528–533.
Sireci, O. (2006). Turkish State Meteorological Service Radar Network Feasibility Studies. Technical Report Turkish State Meteorological Service.
Sohani, A., Naderi, S., Torabi, F., et al. (2020a). Application based multi-objective performance optimization of a proton exchange membrane fuel cell. Journal of Cleaner Production, 252, 119567. https://doi.org/10.1016/j.jclepro.2019.119567
Sohani, A., Sayyaadi, H., Cornaro, C., et al. (2022). Using machine learning in photovoltaics to create smarter and cleaner energy generation systems: A comprehensive review. Journal of Cleaner Production, 364, 132701. https://doi.org/10.1016/j.jclepro.2022.132701
Sohani, A., Shahverdian, M. H., Sayyaadi, H., et al. (2020b). Impact of absolute and relative humidity on the performance of mono and poly crystalline silicon photovoltaics; applying artificial neural network. Journal of Cleaner Production, 276, 123016. https://doi.org/10.1016/j.jclepro.2020.123016
Souley Agbodjan, Y., Wang, J., Cui, Y., et al. (2022). Bibliometric analysis of zero energy building research, challenges and solutions. Solar Energy, 244, 414–433. https://doi.org/10.1016/j.solener.2022.08.061
Venkatakrishnan, G. R., Rengaraj, R., Tamilselvi, S., et al. (2023). Detection, location, and diagnosis of different faults in large solar PV system—a review. International Journal of Low-Carbon Technologies, 18, 659–674. https://doi.org/10.1093/ijlct/ctad018
Venkatasubramanian, V. (2018). The promise of artificial intelligence in chemical engineering: Is it here, finally? AIChE Journal, 65(2), 466–478. Portico. https://doi.org/10.1002/aic.16489
Voss, K., Riley, M. (2009). IEA joint project: towards net zero energy solar buildings (NZEBs). International Energy Agency.
Voyant, C., Notton, G., Kalogirou, S., et al. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569–582. https://doi.org/10.1016/j.renene.2016.12.095
Wang, F., Zhen, Z., Mi, Z., et al. (2015). Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting. Energy and Buildings, 86, 427–438. https://doi.org/10.1016/j.enbuild.2014.10.002
Wolff, B., Kühnert, J., Lorenz, E., et al. (2016). Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data. Solar Energy, 135, 197–208. https://doi.org/10.1016/j.solener.2016.05.051
Wu, Y., Lan, Q., & Sun, Y. (2009). Application of BP neural network fault diagnosis in solar photovoltaic system. In: Proceedings of 2009 International Conference on Mechatronics and Automation; 9–12 August 2009; Changchun, China. pp. 2581–2585.
Yaïci, W., & Entchev, E. (2016). Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system. Renewable Energy, 86, 302–315. https://doi.org/10.1016/j.renene.2015.08.028
Zakari, A., Khan, I., Tan, D., et al. (2022). Energy efficiency and sustainable development goals (SDGs). Energy, 239, 122365. https://doi.org/10.1016/j.energy.2021.122365
Zamo, M., Mestre, O., Arbogast, P., et al. (2014). A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production. Solar Energy, 105, 792–803. https://doi.org/10.1016/j.solener.2013.12.006
Zang, H., Xu, Q., & Bian, H. (2012). Generation of typical solar radiation data for different climates of China. Energy, 38(1), 236–248. https://doi.org/10.1016/j.energy.2011.12.008
Zenebe, T. M., Midtgård, O. M., Völler, S., Cali, Ü. (2022). Machine Learning for PV System Operational Fault Analysis: Literature Review. In: Sanfilippo, F., Granmo, O. C., Yayilgan, S. Y., Bajwa, I. S. (editors). Intelligent Technologies and Applications. Springer. pp. 337–351.
Zhang, Q., Cui, N., Feng, Y., et al. (2018). Comparative Analysis of Global Solar Radiation Models in Different Regions of China. Advances in Meteorology, 2018, 1–21. https://doi.org/10.1155/2018/3894831
Ziemba, P., & Szaja, M. (2023). Fuzzy Decision-Making Model for Solar Photovoltaic Panel Evaluation. Energies, 16(13), 5161. https://doi.org/10.3390/en16135161
Zou, L., Wang, L., Xia, L., et al. (2017). Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems. Renewable Energy, 106, 343–353. https://doi.org/10.1016/j.renene.2017.01.042
DOI: https://doi.org/10.24294/jipd9940
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