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

Universidad del Pacífico

Research output: Contribution to journal › Article in a journal › peer-review

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
Pages (from-to)	181-194
Number of pages	14
Journal	Communications in Computer and Information Science
Volume	1410
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/

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Neural networks
Solar radiation
Weather station
Wind speed

Access to Document

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

Cite this

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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.",

keywords = "Neural networks, Solar radiation, Weather station, Wind speed",

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",

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

language = "English",

volume = "1410",

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journal = "Communications in Computer and Information Science",

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TY - JOUR

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 - http://www.scopus.com/inward/record.url?scp=85111123223&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/a49916da-dc33-3040-aac9-17fb8a4e1f48/

U2 - 10.1007/978-3-030-76228-5_13

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

M3 - Article in a journal

VL - 1410

SP - 181

EP - 194

JO - Communications in Computer and Information Science

JF - Communications in Computer and Information Science

SN - 1865-0929

T2 - International Conference on Information Management and Big Data

Y2 - 1 October 2020 through 3 October 2020

ER -

Prediction of solar radiation using neural networks forecasting

Abstract

Bibliographical note

Keywords

Access to Document

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

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