{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:30:50Z","timestamp":1743129050000,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031525162"},{"type":"electronic","value":"9783031525179"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-52517-9_12","type":"book-chapter","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T13:02:46Z","timestamp":1706706166000},"page":"174-188","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimation of\u00a0the\u00a0Performance of\u00a0Photovoltaic Cells by\u00a0Means of\u00a0an\u00a0Adaptative Neural Fuzzy Inference Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5569-3532","authenticated-orcid":false,"given":"Hector Felipe","family":"Mateo-Romero","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6741-6145","authenticated-orcid":false,"given":"Mario Eduardo Carbon\u00f3 dela","family":"Rosa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8822-2948","authenticated-orcid":false,"given":"Luis","family":"Hern\u00e1ndez-Callejo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9130-7339","authenticated-orcid":false,"given":"Miguel \u00c1ngel","family":"Gonz\u00e1lez-Rebollo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1460-158X","authenticated-orcid":false,"given":"Valent\u00edn","family":"Carde\u00f1oso-Payo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5107-4892","authenticated-orcid":false,"given":"Victor","family":"Alonso-G\u00f3mez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2283-0350","authenticated-orcid":false,"given":"\u00d3scar","family":"Mart\u00ednez-Sacrist\u00e1n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2834-5591","authenticated-orcid":false,"given":"Sara","family":"Gallardo-Saavedra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,1]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Acciani, G., Falcone, O., Vergura, S.: Typical defects of PV-cells. In: 2010 IEEE International Symposium on Industrial Electronics, pp. 2745\u20132749 (2010). https:\/\/doi.org\/10.1109\/ISIE.2010.5636901","key":"12_CR1","DOI":"10.1109\/ISIE.2010.5636901"},{"key":"12_CR2","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi, L., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 53 (2021). https:\/\/doi.org\/10.1186\/s40537-021-00444-8","journal-title":"J. Big Data"},{"doi-asserted-by":"publisher","unstructured":"Chawla, R., Singal, P., Garg, A.K.: A Mamdani fuzzy logic system to enhance solar cell micro-cracks image processing. 3D Res. 9, 1\u201312 (2018). https:\/\/doi.org\/10.1007\/S13319-018-0186-7\/METRICS. https:\/\/link.springer.com\/article\/10.1007\/s13319-018-0186-7","key":"12_CR3","DOI":"10.1007\/S13319-018-0186-7\/METRICS"},{"doi-asserted-by":"publisher","unstructured":"Hern\u00e1ndez-Callejo, L., Gallardo-Saavedra, S., Alonso-G\u00f3mez, V.: A review of photovoltaic systems: design, operation and maintenance. Solar Energy 188, 426\u2013440 (2019). https:\/\/doi.org\/10.1016\/j.solener.2019.06.017. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0038092X19305912","key":"12_CR4","DOI":"10.1016\/j.solener.2019.06.017"},{"doi-asserted-by":"publisher","unstructured":"Jahic, A., Konjic, T., Pihler, J., Jahic, A.: Photovoltaic power output forecasting with ANFIS. In: Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016), pp. 1\u20138 (2016). https:\/\/doi.org\/10.1049\/cp.2016.1056","key":"12_CR5","DOI":"10.1049\/cp.2016.1056"},{"issue":"3","key":"12_CR6","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"JS Jang","year":"1993","unstructured":"Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665\u2013685 (1993). https:\/\/doi.org\/10.1109\/21.256541","journal-title":"IEEE Trans. Syst. Man Cybern."},{"doi-asserted-by":"publisher","unstructured":"Karaboga, D., Kaya, E.: Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif. Intell. Rev. 52(4), 2263\u20132293 (2019). https:\/\/doi.org\/10.1007\/s10462-017-9610-2","key":"12_CR7","DOI":"10.1007\/s10462-017-9610-2"},{"doi-asserted-by":"publisher","unstructured":"Khosrojerdi, F., Taheri, S., Cretu, A.M.: An adaptive neuro-fuzzy inference system-based MPPT controller for photovoltaic arrays. In: 2016 IEEE Electrical Power and Energy Conference (EPEC), pp. 1\u20136 (2016). https:\/\/doi.org\/10.1109\/EPEC.2016.7771794","key":"12_CR8","DOI":"10.1109\/EPEC.2016.7771794"},{"unstructured":"\u00d2scar Lorente, Riera, I., Rana, A.: Image classification with classic and deep learning techniques (2021)","key":"12_CR9"},{"doi-asserted-by":"publisher","unstructured":"Mateo\u00a0Romero, H.F., et al.: Applications of artificial intelligence to photovoltaic systems: a review. Appl. Sci. 12(19) (2022). https:\/\/doi.org\/10.3390\/app121910056","key":"12_CR10","DOI":"10.3390\/app121910056"},{"doi-asserted-by":"publisher","unstructured":"Mateo-Romero, H.F., et al.: Synthetic dataset of electroluminescence images of photovoltaic cells by deep convolutional generative adversarial networks. Sustainability 15(9), 7175 (2023). https:\/\/doi.org\/10.3390\/su15097175","key":"12_CR11","DOI":"10.3390\/su15097175"},{"doi-asserted-by":"publisher","unstructured":"Mellit, A., Kalogirou, S.A.: Artificial intelligence techniques for photovoltaic applications: a review. Prog. Energy Combust. Sci. 34(5), 574\u2013632 (2008). https:\/\/doi.org\/10.1016\/j.pecs.2008.01.001. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0360128508000026","key":"12_CR12","DOI":"10.1016\/j.pecs.2008.01.001"},{"doi-asserted-by":"publisher","unstructured":"Mellit, A., Kalogirou, S.A.: ANFIS-based modelling for photovoltaic power supply system: a case study. Renew. Energy 36(1), 250\u2013258 (2011). https:\/\/doi.org\/10.1016\/j.renene.2010.06.028. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0960148110002843","key":"12_CR13","DOI":"10.1016\/j.renene.2010.06.028"},{"doi-asserted-by":"publisher","unstructured":"Morales-Aragon\u00e9s, J.I., et al.: Low-cost three-quadrant single solar cell I\u2013V tracer. Appl. Sci. 12(13) (2022). https:\/\/doi.org\/10.3390\/app12136623","key":"12_CR14","DOI":"10.3390\/app12136623"},{"issue":"2","key":"12_CR15","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/JPHOTOV.2019.2892189","volume":"9","author":"DS Pillai","year":"2019","unstructured":"Pillai, D.S., Blaabjerg, F., Rajasekar, N.: A comparative evaluation of advanced fault detection approaches for PV systems. IEEE J. Photovolt. 9(2), 513\u2013527 (2019). https:\/\/doi.org\/10.1109\/JPHOTOV.2019.2892189","journal-title":"IEEE J. Photovolt."},{"unstructured":"REN21: Renewables 2022 Global Status Report. REN21 (2022). https:\/\/www.ren21.net\/","key":"12_CR16"},{"doi-asserted-by":"publisher","unstructured":"Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybernet. SMC-15(1), 116\u2013132 (1985). https:\/\/doi.org\/10.1109\/TSMC.1985.6313399","key":"12_CR17","DOI":"10.1109\/TSMC.1985.6313399"}],"container-title":["Communications in Computer and Information Science","Smart Cities"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-52517-9_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T13:06:01Z","timestamp":1706706361000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-52517-9_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031525162","9783031525179"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-52517-9_12","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSC-Cities","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ibero-American Congress of Smart Cities","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico City and Cuernavaca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icsccities2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icsc-cities.com","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}