Photovoltaic energy yield predictions using satellite data
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 tempera...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SPIE-Intl Soc Optical Eng
2021
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| Online Access: | https://hdl.handle.net/1721.1/138477 |
| _version_ | 1826217002432200704 |
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| author | Peters, Ian Marius Liu, Haohui Buonassisi, Tonio |
| author_facet | Peters, Ian Marius Liu, Haohui Buonassisi, Tonio |
| author_sort | Peters, Ian Marius |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:56:29Z |
| format | Article |
| id | mit-1721.1/138477 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:56:29Z |
| publishDate | 2021 |
| publisher | SPIE-Intl Soc Optical Eng |
| record_format | dspace |
| spelling | mit-1721.1/1384772021-12-15T03:01:06Z Photovoltaic energy yield predictions using satellite data Peters, Ian Marius Liu, Haohui Buonassisi, Tonio 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. 2021-12-14T19:12:39Z 2021-12-14T19:12:39Z 2020 2021-12-14T19:09:36Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/138477 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. en 10.1117/12.2557375 Proceedings of SPIE - The International Society for Optical Engineering 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. application/pdf SPIE-Intl Soc Optical Eng SPIE |
| spellingShingle | Peters, Ian Marius Liu, Haohui Buonassisi, Tonio Photovoltaic energy yield predictions using satellite data |
| title | Photovoltaic energy yield predictions using satellite data |
| title_full | Photovoltaic energy yield predictions using satellite data |
| title_fullStr | Photovoltaic energy yield predictions using satellite data |
| title_full_unstemmed | Photovoltaic energy yield predictions using satellite data |
| title_short | Photovoltaic energy yield predictions using satellite data |
| title_sort | photovoltaic energy yield predictions using satellite data |
| url | https://hdl.handle.net/1721.1/138477 |
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