Prediction of solar radiation using neural networks forecasting

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

doi:10.1007/978-3-030-76228-5_13

Prediction of solar radiation using neural networks forecasting

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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

2 Citas (Scopus)

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 original	Inglés
Título de la publicación alojada	Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
Editores	Juan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
Lugar de publicación	Cham
Páginas	181-194
Número de páginas	14
ISBN (versión digital)	978-3-030-76228-5
DOI	https://doi.org/10.1007/978-3-030-76228-5_13
Estado	Publicada - 12 may. 2021
Evento	International Conference on Information Management and Big Data - Lima, Perú Duración: 1 oct. 2020 → 3 oct. 2020 Número de conferencia: 7 https://simbig.org/SIMBig2020/

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1410 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	International Conference on Information Management and Big Data
Título abreviado	SIMBig
País/Territorio	Perú
Ciudad	Lima
Período	1/10/20 → 3/10/20
Dirección de internet	https://simbig.org/SIMBig2020/

Nota bibliográfica

Contribución a la conferencia.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1007/978-3-030-76228-5_13

Otros archivos y enlaces

2 Citas de Scopus
1 Libro

Information management and big data: 7th Annual International Conference, SIMBig 2020, Lima, Peru, October 1–3, 2020, Proceedings
Lossio-Ventura, J. A., Valverde-Rebaza, J. C. (Editor), Díaz, E. (Editor) & Alatrista-Salas, H. (Editor), 2021, Cham. 554 p. (Communications in Computer and Information Science; vol. 1410 CCIS)
Producción científica: Informe/libro › Libro › revisión exhaustiva

Citar esto

Talavera, Á., Ponce-Jara, M., Velásquez, C., & Tonato Peralta, D. (2021). Prediction of solar radiation using neural networks forecasting. En J. A. Lossio-Ventura, J. C. Valverde-Rebaza, E. Díaz, & H. Alatrista-Salas (Eds.), Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings (pp. 181-194). (Communications in Computer and Information Science; Vol. 1410 CCIS).. https://doi.org/10.1007/978-3-030-76228-5_13

Talavera, Álvaro ; Ponce-Jara, Marcos ; Velásquez, Carlos et al. / Prediction of solar radiation using neural networks forecasting. Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. editor / Juan Antonio Lossio-Ventura ; Jorge Carlos Valverde-Rebaza ; Eduardo Díaz ; Hugo Alatrista-Salas. Cham, 2021. pp. 181-194 (Communications in Computer and Information Science).

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title = "Prediction of solar radiation using neural networks forecasting",

abstract = "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.",

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Talavera, Á, Ponce-Jara, M, Velásquez, C & Tonato Peralta, D 2021, Prediction of solar radiation using neural networks forecasting. En JA Lossio-Ventura, JC Valverde-Rebaza, E Díaz & H Alatrista-Salas (eds.), Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. Communications in Computer and Information Science, vol. 1410 CCIS, Cham, pp. 181-194, International Conference on Information Management and Big Data, Lima, Perú, 1/10/20. https://doi.org/10.1007/978-3-030-76228-5_13

Prediction of solar radiation using neural networks forecasting. / Talavera, Álvaro; Ponce-Jara, Marcos; Velásquez, Carlos et al.
Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. ed. / Juan Antonio Lossio-Ventura; Jorge Carlos Valverde-Rebaza; Eduardo Díaz; Hugo Alatrista-Salas. Cham, 2021. p. 181-194 (Communications in Computer and Information Science; Vol. 1410 CCIS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Prediction of solar radiation using neural networks forecasting

AU - Talavera, Álvaro

AU - Ponce-Jara, Marcos

AU - Velásquez, Carlos

AU - Tonato Peralta, David

N1 - Conference code: 7

PY - 2021/5/12

Y1 - 2021/5/12

N2 - 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.

AB - 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.

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KW - Solar radiation

KW - Weather station

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BT - Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings

A2 - Lossio-Ventura, Juan Antonio

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A2 - Díaz, Eduardo

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CY - Cham

T2 - International Conference on Information Management and Big Data

Y2 - 1 October 2020 through 3 October 2020

ER -

Talavera Á, Ponce-Jara M, Velásquez C, Tonato Peralta D. Prediction of solar radiation using neural networks forecasting. En Lossio-Ventura JA, Valverde-Rebaza JC, Díaz E, Alatrista-Salas H, editores, Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. Cham. 2021. p. 181-194. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-76228-5_13

Prediction of solar radiation using neural networks forecasting

Resumen

Serie de la publicación

Conferencia

Nota bibliográfica

ODS de las Naciones Unidas

Acceder al documento

Otros archivos y enlaces

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Producción científica

Information management and big data: 7th Annual International Conference, SIMBig 2020, Lima, Peru, October 1–3, 2020, Proceedings

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