{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T08:00:50Z","timestamp":1775376050938,"version":"3.50.1"},"reference-count":12,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T00:00:00Z","timestamp":1581379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Photovoltaic (PV) system energy production is non-linear because it is influenced by the random nature of weather conditions. The use of machine learning techniques to model the PV system energy production is recommended since there is no known way to deal well with non-linear data. In order to detect PV system faults, the machine learning models should provide accurate outputs. The aim of this work is to accurately predict the DC energy of six PV strings of a utility-scale PV system and to accurately detect PV string faults by benchmarking the results of four machine learning methodologies known to improve the accuracy of the machine learning models, such as the data mining methodology, machine learning technique benchmarking methodology, hybrid methodology, and the ensemble methodology. A new hybrid methodology is proposed in this work which combines the use of a fuzzy system and the use of a machine learning system containing five different trained machine learning models, such as the regression tree, artificial neural networks, multi-gene genetic programming, Gaussian process, and support vector machines for regression. The results showed that the hybrid methodology provided the most accurate machine learning predictions of the PV string DC energy, and consequently the PV string fault detection is successful.<\/jats:p>","DOI":"10.3390\/e22020205","type":"journal-article","created":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T11:45:30Z","timestamp":1581421530000},"page":"205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Machine Learning Photovoltaic String Analyzer"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1989-4852","authenticated-orcid":false,"given":"Sandy","family":"Rodrigues","sequence":"first","affiliation":[{"name":"Instituto de Telecomunicacoes of the Instituto Superior Tecnico of the University of Lisbon, 1049-001 Lisbon, Portugal"},{"name":"Laboratory for Robotics and Systems in Engineering (LARSyS), Madeira Interactive Technologies (M-ITI) and Institute and Interactive Technologies Institute (ITI), 9020-105 Funchal, Portugal"}]},{"given":"Gerhard","family":"M\u00fctter","sequence":"additional","affiliation":[{"name":"ALTESO GmbH, 1010 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4931-7960","authenticated-orcid":false,"given":"Helena Geirinhas","family":"Ramos","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunicacoes of the Instituto Superior Tecnico of the University of Lisbon, 1049-001 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7334-3993","authenticated-orcid":false,"given":"F.","family":"Morgado-Dias","sequence":"additional","affiliation":[{"name":"Laboratory for Robotics and Systems in Engineering (LARSyS), Madeira Interactive Technologies (M-ITI) and Institute and Interactive Technologies Institute (ITI), 9020-105 Funchal, Portugal"},{"name":"Faculty of Exact Sciences and Engineering, University of Madeira, 9020-105 Funchal, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ali, K., Khan, L., Khan, Q., Ullah, S., Ahmad, S., Mumtaz, S., and Karam, F.W. (2019). Robust Integral Backstepping Based Nonlinear MPPT Control for a PV System. Energies, 12.","DOI":"10.3390\/en12163180"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1016\/j.rser.2017.03.119","article-title":"Environment-adjusted operational performance evaluation of solar photovoltaic power plants: A three stage efficiency analysis","volume":"76","author":"Wang","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/TEC.2015.2431613","article-title":"Backstepping Control of Smart Grid-Connected Distributed Photovoltaic Power Supplies for Telecom Equipment","volume":"30","author":"Martin","year":"2015","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rodrigues, S., Ramos, H.G., and Morgado-Dias, F. (2017, January 25\u201330). Machine Learning in PV Fault Detection, Diagnostics and Prognostics: A Review. Proceedings of the 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC), Washington, DC, USA.","DOI":"10.1109\/PVSC.2017.8366581"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/TSTE.2017.2762435","article-title":"Day-Ahead Hourly Forecasting of Power Generation from Photovoltaic Plants","volume":"9","author":"Gigoni","year":"2018","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jiang, L.L., and Maskell, D.L. (2015, January 12\u201317). Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods. 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Energies, 11.","DOI":"10.3390\/en11061487"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Huertas Tato, J., and Centeno Brito, M. (2018). Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production. Energies, 12.","DOI":"10.3390\/en12010100"},{"key":"ref_11","unstructured":"Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, Basic Books."},{"key":"ref_12","unstructured":"Rodrigues, S., Carvalho, J.P., Geirinhas Ramos, H., and Morgado-Dias, F. (2018, January 24\u201328). A More Accurate Machine Learning Photovoltaic System Performance Analyser by Using Fuzzy Logic. Proceedings of the 35th European Photovoltaic Solar Energy Conference and Exhibition, Brussels, Belgium."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/2\/205\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:56:57Z","timestamp":1760173017000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/2\/205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,11]]},"references-count":12,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["e22020205"],"URL":"https:\/\/doi.org\/10.3390\/e22020205","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,11]]}}}