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 proceedingConference contributionpeer-review

1 Scopus citations


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 languageEnglish
Title of host publicationInformation Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
EditorsJuan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
Place of PublicationCham
Number of pages14
ISBN (Electronic)978-3-030-76228-5
StatePublished - 12 May 2021
EventInternational Conference on Information Management and Big Data - Lima, Peru
Duration: 1 Oct 20203 Oct 2020
Conference number: 7

Publication series

NameCommunications in Computer and Information Science
Volume1410 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


ConferenceInternational Conference on Information Management and Big Data
Abbreviated titleSIMBig
Internet address

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Neural networks
  • Solar radiation
  • Weather station
  • Wind speed


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