The use of ANN and conventional solar-plant meteorological variables to estimate atmospheric horizontal extinction
In the search to optimize electricity generation systems based on renewable energy sources, solar power plants are a clear alternative for reducing pollution on the planet. In particular, concentrating solar power plants with central tower technology supply energy to large population centers and the...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Huelva (UHU) |
| Repositorio: | Arias Montano. Repositorio Institucional de la Universidad de Huelva |
| Idioma: | inglés |
| OAI Identifier: | oai:ariasmontano.uhu.es:10272/24563 |
| Acceso en línea: | https://hdl.handle.net/10272/24563 |
| Access Level: | acceso abierto |
| Palabra clave: | Atmospheric Extinction Solar energy Image processing CSP plants PV plants ANN 2501 Ciencias de la Atmósfera 3322 Tecnología Energética 1203.04 Inteligencia Artificial |
| Sumario: | In the search to optimize electricity generation systems based on renewable energy sources, solar power plants are a clear alternative for reducing pollution on the planet. In particular, concentrating solar power plants with central tower technology supply energy to large population centers and they are generally located at desert sites. Production in these plants drops due to the presence of particles in the environment. These particles are complex to measure and doing so usually requires the use of dedicated, expensive instrumentation. In this work, we present a methodology called Extinction that estimates this attenuation phenomenon utilizing meteorological variables, along with the use of artificial neural networks (ANN). Direct normal irradiance, relative humidity, temperature and pressure have been the only meteorological variables needed to estimate the Extinction. The results from the estimations presented a correlation coefficient value (R) of 0.88 (between the measured and estimated atmospheric horizontal extinction with ANN), a normalized Mean Bias Error (nMBE) of 0% and a normalized Root-Mean Square Deviation (nRMSD) of 7%. |
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