Wind Speed Prediction Study: Practical Application of ANN to Energy Production In Brazilian Territory

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dc.contributor.authorOliveira M.B.
dc.contributor.authorVega-Garcia V.
dc.date.accessioned2024-03-12T19:10:55Z
dc.date.available2024-03-12T19:10:55Z
dc.date.issued2023
dc.description.abstract© 2023 IEEE.This article provides a quick guide for users, using online tools, focusing on wind power generation and choosing the best region for installing wind turbines (WT). Highlighting topographic-map tools for terrain feasibility in the installation of WT and artificial neural networks (ANN) to understand climate data from the region under study and predict future values of the same data. To carry out the proof-of-concept, Python was used in this study with several neural network models. For this study, a climate database extracted from the National Institute of Meteorology and altitude records from the Brazilian Wind Atlas was used for the period from 2015 to 2020. The present study found that the best correlation results for climatic variables and forecasts were proportional to the increase in the database. Several ANN models were tested for wind speed predictions, highlighting a dense and convolutional model (multiple-output method).
dc.description.firstpage520
dc.description.lastpage524
dc.identifier.doi10.1109/ISGT-LA56058.2023.10328323
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34146
dc.relation.ispartof2023 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT-LA 2023
dc.rightsAcesso Restrito
dc.subject.otherlanguageTime Series. Wind Energy. Artificial Neural Network
dc.titleWind Speed Prediction Study: Practical Application of ANN to Energy Production In Brazilian Territory
dc.typeArtigo de evento
local.scopus.citations0
local.scopus.eid2-s2.0-85180003349
local.scopus.subjectClimate data
local.scopus.subjectEnergy productions
local.scopus.subjectOn-line tools
local.scopus.subjectProof of concept
local.scopus.subjectTime series.
local.scopus.subjectTimes series
local.scopus.subjectTopographic map
local.scopus.subjectWind energy.
local.scopus.subjectWind power generation
local.scopus.subjectWind speed prediction
local.scopus.updated2025-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85180003349&origin=inward
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