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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
Editors	Juan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
Place of Publication	Cham
Pages	181-194
Number of pages	14
ISBN (Electronic)	978-3-030-76228-5
DOIs	https://doi.org/10.1007/978-3-030-76228-5_13
State	Published - 12 May 2021
Event	International Conference on Information Management and Big Data - Lima, Peru Duration: 1 Oct 2020 → 3 Oct 2020 Conference number: 7 https://simbig.org/SIMBig2020/

Publication series

Name	Communications in Computer and Information Science
Volume	1410 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	International Conference on Information Management and Big Data
Abbreviated title	SIMBig
Country/Territory	Peru
City	Lima
Period	1/10/20 → 3/10/20
Internet address	https://simbig.org/SIMBig2020/

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 13 Climate Action
SDG 17 Partnerships for the Goals

Keywords

Neural networks
Solar radiation
Weather station
Wind speed

Access to Document

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

Cite this

Talavera, Á., Ponce-Jara, M., Velásquez, C., & Tonato Peralta, D. (2021). Prediction of solar radiation using neural networks forecasting. In 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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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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author = "{\'A}lvaro Talavera and Marcos Ponce-Jara and Carlos Vel{\'a}squez and \{Tonato Peralta\}, David",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; International Conference on Information Management and Big Data, SIMBig ; Conference date: 01-10-2020 Through 03-10-2020",

year = "2021",

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doi = "10.1007/978-3-030-76228-5\_13",

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Talavera, Á, Ponce-Jara, M, Velásquez, C & Tonato Peralta, D 2021, Prediction of solar radiation using neural networks forecasting. in 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, Peru, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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.

KW - Neural networks

KW - Solar radiation

KW - Weather station

KW - Wind speed

UR - https://www.scopus.com/pages/publications/85111123223

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

M3 - Conference contribution

SN - 978-3-030-76227-8

T3 - Communications in Computer and Information Science

SP - 181

EP - 194

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

A2 - Lossio-Ventura, Juan Antonio

A2 - Valverde-Rebaza, Jorge Carlos

A2 - Díaz, Eduardo

A2 - Alatrista-Salas, Hugo

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. In Lossio-Ventura JA, Valverde-Rebaza JC, Díaz E, Alatrista-Salas H, editors, 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

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

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

Cite this