dc.contributor.author |
Huertas Tato, Javier |
|
dc.contributor.author |
Galván León, Inés María |
|
dc.contributor.author |
Aler Mur, Ricardo |
|
dc.contributor.author |
Rodríguez Benítez, Francisco Javier |
|
dc.contributor.author |
Pozo Vázquez, David |
|
dc.date.accessioned |
2021-06-22T18:09:29Z |
|
dc.date.available |
2021-06-22T18:09:29Z |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Huertas-Tato, J., Galván, I. M., Aler, R., Rodríguez-Benítez, F. J., & Pozo-Vázquez, D. (2022). Using a Multi-view Convolutional Neural Network to monitor solar irradiance. Neural Computing and Applications, 34:10295–10307. https://doi.org/10.1007/s00521-021-05959-y |
spa |
dc.identifier.issn |
0941-0643 |
|
dc.identifier.issn |
1433-3058 |
|
dc.identifier.uri |
http://hdl.handle.net/11268/10176 |
|
dc.description.abstract |
In the last years, there is an increasing interest for enhanced method for assessing and monitoring the level of the global horizontal irradiance (GHI) in photovoltaic (PV) systems, fostered by the massive deployment of this energy. Thermopile or photodiode pyranometers provide point measurements, which may not be adequate in cases when areal information is important (as for PV network or large PV plants monitoring). The use of All Sky Imagers paired convolutional neural networks, a powerful technique for estimation, has been proposed as a plausible alternative. In this work, a convolutional neural network architecture is presented to estimate solar irradiance from sets of ground-level Total Sky Images. This neural network is capable of combining images from three cameras. Results show that this approach is more accurate than using only images from a single camera. It has also been shown to improve the performance of two other approaches: a cloud fraction model and a feature extraction model. |
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dc.description.sponsorship |
Sin financiación |
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dc.language.iso |
eng |
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dc.title |
Using a Multi-view Convolutional Neural Network to monitor solar irradiance |
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dc.type |
article |
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dc.description.impact |
5.102 JCR (2021) Q2, 45/145 Computer Science, Artificial Intelligence |
spa |
dc.description.impact |
1.072 SJR (2021) Q1, 104/404 Software |
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dc.description.impact |
No data IDR 2021 |
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dc.identifier.doi |
10.1007/s00521-021-05959-y |
|
dc.rights.accessRights |
closedAccess |
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dc.subject.unesco |
Análisis de datos |
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dc.subject.unesco |
Inteligencia artificial |
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dc.subject.unesco |
Ciencias del espacio |
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dc.description.filiation |
UEM |
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dc.relation.publisherversion |
http://ezproxy.universidadeuropea.es/login?url=http:/ /dx.doi.org/10.1007/s00521-021-05959-y |
spa |
dc.peerreviewed |
Si |
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