Prediction of solar radiation using neural networks forecasting

Álvaro Talavera, Marcos Ponce-Jara, Carlos Velásquez, David Tonato Peralta

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Solar radiation and wind data play an important role in renewable energy projects to produce electricity. In Ecuador, these data are not always available for locations of interest due to absences of meteorological stations. In the scope of this paper, a low-cost automatic meteorological station prototype based on Raspberry technology was developed to measure the aforementioned variables. The objective of this paper is twofold: a) to present a proposal for the design of a low-cost automatic weather station using the Raspberry Pi microcomputer, showing the feasibility of this technology as an alternative for the construction of automatic meteorological station and; b) to use Forecasting with neural networks to predict solar radiation in Manta, Ecuador, based on the historical data collected: solar radiation, wind speed and wind direction. We proved that both technology feasibility and Machine learning has a high potential as a tool to use in this field of study.
Idioma originalInglés
Título de la publicación alojadaInformation management and big data
Subtítulo de la publicación alojada7th Annual International Conference, SIMBig 2020, Lima, Peru, October 1–3, 2020, Proceedings
EditoresJuan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
Lugar de publicaciónCham
EditorialSpringer
Páginas181-194
Número de páginas14
ISBN (versión digital)978-3-030-76228-5
ISBN (versión impresa)978-3-030-76227-8
DOI
EstadoPublicada - 12 may 2021
EventoInternational Conference on Information Management and Big Data - Lima, Perú
Duración: 1 oct 20203 oct 2020
Número de conferencia: 7
https://simbig.org/SIMBig2020/

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1410 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

ConferenciaInternational Conference on Information Management and Big Data
Título abreviadoSIMBig
País/TerritorioPerú
CiudadLima
Período1/10/203/10/20
Dirección de internet

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Contribución a la conferencia.

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© 2021, Springer Nature Switzerland AG.

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