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Photovoltaic energy yield predictions using satellite data
| dc.contributor.author | Peters, Ian Marius | |
| dc.contributor.author | Liu, Haohui | |
| dc.contributor.author | Buonassisi, Tonio | |
| dc.date.accessioned | 2021-12-14T19:12:39Z | |
| dc.date.available | 2021-12-14T19:12:39Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/138477 | |
| dc.description.abstract | Energy 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.iso | en | |
| dc.publisher | SPIE-Intl Soc Optical Eng | en_US |
| dc.relation.isversionof | 10.1117/12.2557375 | en_US |
| dc.rights | Article 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.source | SPIE | en_US |
| dc.title | Photovoltaic energy yield predictions using satellite data | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Peters, 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.journal | Proceedings of SPIE - The International Society for Optical Engineering | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
| dc.date.updated | 2021-12-14T19:09:36Z | |
| dspace.orderedauthors | Peters, IM; Liu, H; Buonassisi, T | en_US |
| dspace.date.submission | 2021-12-14T19:09:37Z | |
| mit.journal.volume | 11366 | en_US |
| mit.license | PUBLISHER_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
