Título:
Analysis of ARMA solar forecasting models using ground measurements and satellite images
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Autor:
Marchesoni-Acland, Franco
Lauret, Philippe
Gómez, Alvaro
Alonso-Suárez, Rodrigo
Lauret, Philippe
Gómez, Alvaro
Alonso-Suárez, Rodrigo
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Tipo de documento:
Preprint
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Palabras clave:
Forecasting
Solar irradiance
Adaptive filters
Satellite images
Solar irradiance
Adaptive filters
Satellite images
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Año de publicación:
2019
Contenido:
Resumen:
As the solar photovoltaic (PV) share in the electricity
grid is growing year by year, solar irradiance forecasting
is becoming increasingly important. In this work the performance
of a recursive formulation of ARMA models suitable for
operational context using the Pampa Húmeda region as a case
study is analyzed. Results are promising, as this simple adaptive
algorithm does not require historical data and outperform
persistence at all lead times. The improvement produced by
adding satellite cloudiness data and short-term local variability
as exogenous inputs is also evaluated. It is found that the
spatially averaged satellite albedo is a useful input variable,
improving the forecast performance, while the introduction of
short-term variability produce negligible performance changes
under this kind of models.
Descripción:
Trabajo presentado a la 46th IEEE PV Specialist Conference, 16-22 de Junio, Chicago, USA, 2019
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Citación:
Marchesoni-Acland, F, Lauret, P, Gómez, A y otros."Analysis of ARMA solar forecasting models using ground measurements and satellite images" [Preprint] Publicado en las Actas de la 46th IEEE PV Specialist Conference, Chicago, USA, 2019.
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Licencia:
Licencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC - By-NC-ND)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| ARMAX_RLS_Uruguay.pdf | — | 647.09 KB | Adobe PDF |