Evidence map›Paper›PMID 41620443›Full record

ArticleScientific reports2026

A genetic algorithm-based ensemble framework for wind speed forecasting.

Tathiana Mikamura Barchi, João Lucas Ferreira Dos Santos, Thiago Antonini Alves, Paulo S G de Mattos Neto, Sergio Luiz Stevan, Fernanda Cristina Corrêa, Marie Chantelle C Medina, Hugo Valadares Siqueira, João Fausto L de Oliveira

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Tathiana Mikamura Barchi *Graduate Program in Industrial Engineering, Federal University of Technology - Paraná, 84017-220, Ponta Grossa, Brazil.
João Lucas Ferreira Dos Santos *Graduate Program in Industrial Engineering, Federal University of Technology - Paraná, 84017-220, Ponta Grossa, Brazil.
Thiago Antonini Alves *Graduate Program in Mechanical Engineering, Federal University of Technology - Paraná, 84017-220, Ponta Grossa, Brazil.
Paulo S G de Mattos Neto *Centro de Informatica, Universidade Federal de Pernambuco, 50740-560, Recife, Brazil.
Sergio Luiz Stevan *Graduate Program in Electric Engineering, Federal University of Technology - Paraná, 84017-220, Ponta Grossa, Brazil.
Fernanda Cristina Corrêa *Graduate Program in Electric Engineering, Federal University of Technology - Paraná, 84017-220, Ponta Grossa, Brazil.
Marie Chantelle C Medina *Universidade de Pernambuco, 50100-010, Recife, Brazil.
Hugo Valadares Siqueira *Graduate Program in Industrial Engineering, Federal University of Technology - Paraná, 84017-220, Ponta Grossa, Brazil.
João Fausto L de Oliveira *Universidade de Pernambuco, 50100-010, Recife, Brazil. hugosiqueira@utfpr.edu.br.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 312367/2022-8Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 001Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco APQ-0955-1.03/24
6 · The paper itself

Abstract

Wind energy has gained significant attention as a clean, renewable resource due to fossil fuels' environmental impact. Accurate wind speed forecasting is essential to address variability and intermittency challenges. Current forecasting difficulties arise from wind speed's high susceptibility to meteorological conditions. This study proposes a GA-based ensemble framework that combines forecasting models using genetic algorithms. We systematically compared 14 models: linear models (AR, ARMA), advanced neural networks (MLP, RBF), hybrid models, and ensembles. Models were evaluated using minute-by-minute data from five major Brazilian cities: Brasília, Florianópolis, Petrolina, Natal, and São Luís. Key findings include: I) Superior Performance: The proposed framework achieved MSE values from 0.0802 to 0.9020 and MAE values from 0.1970 to 0.6140 across all datasets; II) Robust Prediction:

Indexed as

Artificial neural networksBox & Jenkins methodsEnsemblesGenetic algorithmHybrid modelsWind speed prediction

Identifiers

PMID41620443
PMCPMC12916787

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.