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dc.contributor.authorPeters, Ian Marius
dc.contributor.authorLiu, Haohui
dc.contributor.authorBuonassisi, Tonio
dc.date.accessioned2021-12-14T19:12:39Z
dc.date.available2021-12-14T19:12:39Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/138477
dc.description.abstractEnergy yield is a key metric for evaluating the performance of photovoltaic systems. It describes the total amount of energy generated by a photovoltaic (PV) installation over a given period, typically a year, and depends on physical properties of the solar cell like efficiency, band gap and temperature coefficient, as well as the operating conditions in a given location. Because the response of a solar cell to these conditions varies, two photovoltaic technologies may have a different energy yield, even if their lab efficiency is identical. Predicting energy yield accurately is important to system operators and installers to estimate the technical and economic performance of a PV installation. In this paper, we summarize our findings about satellite based energy yield predictions of solar cells with various technologies.en_US
dc.language.isoen
dc.publisherSPIE-Intl Soc Optical Engen_US
dc.relation.isversionof10.1117/12.2557375en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSPIEen_US
dc.titlePhotovoltaic energy yield predictions using satellite dataen_US
dc.typeArticleen_US
dc.identifier.citationPeters, Ian Marius, Liu, Haohui and Buonassisi, Tonio. 2020. "Photovoltaic energy yield predictions using satellite data." Proceedings of SPIE - The International Society for Optical Engineering, 11366.
dc.relation.journalProceedings of SPIE - The International Society for Optical Engineeringen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-12-14T19:09:36Z
dspace.orderedauthorsPeters, IM; Liu, H; Buonassisi, Ten_US
dspace.date.submission2021-12-14T19:09:37Z
mit.journal.volume11366en_US
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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