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Predicting Module Performance from Cell and Module Parameters Using Machine Learning

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dc.contributor.authorErnst, Marcoen
dc.contributor.authorWagner-Mohnsen, Hannesen
dc.contributor.authorWasmer, Svenen
dc.contributor.authorKlöter, Bernharden
dc.contributor.authorAltermatt, Pietro P.en
dc.date.accessioned2026-07-02T22:41:46Z
dc.date.available2026-07-02T22:41:46Z
dc.date.issued2023en
dc.description.abstractWe use machine learning and device physics to analyze mass-produced solar modules, identifying factors that affect performance. Our approach is demonstrated by simulating 10,000 PERC solar cells and 2,000 half-cell modules using numerical device simulations. Our flexible approach can be applied to real data from production lines and scenarios.en
dc.description.statusPeer-revieweden
dc.format.extent3en
dc.identifier.scopus85192396631en
dc.identifier.scopus85193071314en
dc.identifier.urihttps://hdl.handle.net/1885/733812287
dc.language.isoenen
dc.relation.ispartofseries2023 Integrated Photonics Research, Silicon and Nanophotonics, IPR 2023 in Advanced Photonics Congress - Part of Advanced Photonics Congress 2023en
dc.rightsPublisher Copyright: © Optica Publishing Group 2023 © 2023 The Author(s).en
dc.titlePredicting Module Performance from Cell and Module Parameters Using Machine Learningen
dc.typeConference paperen
dspace.entity.typePublicationen
local.contributor.affiliationErnst, Marco; School of Engineering, ANU College of Systems and Society, The Australian National Universityen
local.contributor.affiliationWagner-Mohnsen, Hannes; WAVELABS Solar Metrology Systems GmbHen
local.contributor.affiliationWasmer, Sven; WAVELABS Solar Metrology Systems GmbHen
local.contributor.affiliationKlöter, Bernhard; WAVELABS Solar Metrology Systems GmbHen
local.contributor.affiliationAltermatt, Pietro P.; Trina Solar Co., Ltd.en
local.identifier.doi10.1364/IPRSN.2023.JW2E.5en
local.identifier.pure4f082f58-616d-4e41-a1e3-88aab1d02d97en
local.identifier.urlhttps://www.scopus.com/pages/publications/85192396631en
local.identifier.urlhttps://opg.optica.org/abstract.cfm?uri=SPPCom-2023-JW2E.5en
local.type.statusPublisheden

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