Methodi Ordinatio 2.0. Wind fore Hybrid and ensemble models for wind power generation forecasting

a systematic review

Authors

  • Matheus Schrippe Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, Paraná, Brasil.
  • Flavio Trojan Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, Paraná, Brasil. https://orcid.org/0000-0003-2274-5321
  • Fernando José Avancini Schenatto Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, Paraná, Brasil. https://orcid.org/0000-0002-3717-2370

DOI:

https://doi.org/10.14488/1676-1901.v26i2.5634

Keywords:

Methodi Ordinatio 2.0, Wind forecast, Hybrid Ensemble

Abstract

The forecasting of wind power generation is essential to support decision-making related to the use of less sustainable and/or costlier sources energy sources. This study conducted a literature review on hybrid and ensemble methods for wind power generation forecasting using the Methodi Ordinatio 2.0. A total of 31 articles were selected for the portfolio, where the use of preprocessing, forecasting methods, and performance metrics was evaluated. The articles in the portfolio show a preference for data preprocessing, mainly through Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and their variants. A similar pattern was observed in the forecasting methods, a wide range of methods was identified. As for performance metrics, although there is also considerable variety, all articles employed either Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE). Of the articles, 70.97% are from Chinese universities, and 29.03% of the total portfolio are from the journal Energy.

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Author Biographies

Matheus Schrippe, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, Paraná, Brasil.

Mestrando em Engenharia de Produção e Sistemas na Universidade Tecnológica Federal do Paraná (UTFPR) de Pato Branco. Pós-graduado em Engenharia de Segurança do Trabalho pela Descomplica e Engenheiro de Produção pela Universidade Tecnológica Federal do Paraná (UTFPR) de Medianeira. Previsão de demanda de geração eólica, utilização de modelos híbridos e ensemble, segurança do trabalho, ergonomia e mercado financeiro.

Flavio Trojan, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, Paraná, Brasil.

Pós-doutorado na Universidade Federal de Pernambuco - PPGEP, Doutor em Engenharia de Produção pela UFPE - Universidade Federal de Pernambuco - PPGEP. Mestre em Engenharia de Produção pela UTFPR - PPGEP, Especialização em Gestão Industrial pelo CEFET-PR, Graduação em Ciências Econômicas pela Universidade Estadual de Ponta Grossa- UEPG, Graduação em Tecnologia Eletrônica (Automação Industrial) pelo CEFET-PR. É Professor Titular da UTFPR nos cursos superiores de Engenharia Elétrica e Tecnologia em Automação Industrial. Professor permanente no Programa de pós-graduação em Engenharia de Produção (PPGEP - Campus Ponta Grossa) e professor permanente no Programa de pós-graduação em Engenharia de Produção e Sistemas (PPGEPS - Campus Pato Branco). Editor Chefe da Revista Gestão Industrial. Atuou como mantenedor do Sistema de Automação da Companhia de Saneamento do Paraná e foi Professor Colaborador na Universidade Estadual de Ponta Grossa - UEPG. Tem experiência na área de Engenharia de Produção com os temas: Pesquisa Operacional, Apoio a Decisão e Decisão Multicritério e Engenharia Elétrica, com ênfase em Automação Industrial, atuando principalmente nos seguintes temas: Informática Industrial, Automação, Sistemas Supervisórios, Controle de Processos, Saneamento Básico, Gestão da Manutenção, Tecnologia da Informação e ainda experiência em Economia com os temas: Crescimento Econômico Brasileiro, Economia Matemática e Estatística Econômica.

Fernando José Avancini Schenatto, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, Paraná, Brasil.

Doutor em Engenharia de Produção na Universidade Federal de Santa Catarina, Mestre em Engenharia de Produção na Universidade Federal de Santa Catarina, Especialização em Educação Matemática Fundação Comunitária Educacional e Cultural Patrocinio, Graduação em Engenharia Elétrica Universidade Católica de Pelotas. Professor titular da Universidade Tecnológica Federal do Paraná. Tem experiência na área de Engenharia de Produção, com ênfase em Gestão da Inovação Tecnológica, atuando principalmente nos seguintes temas: gestão da inovação; gestão de tecnologia; estratégia tecnológica; prospectiva estratégica; arranjos produtivos locais; e incubadoras de empresas.

References

AI, C. et al. Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm. Chaos, Solitons and Fractals, v. 177, 114222, 2023.

ASLAM, M. KIM, J.-S. JUNG, J. Multi-step ahead wind power forecasting based on dual-attention mechanism. Energy Reports, v. 9, p. 239-251, 2023.

CASCIARO, G. et al. Novel strategies of Ensemble Model Output Statistics (EMOS) for calibrating wind speed/power forecasts. Energy Conversion and Management, v. 271, 116297, 2022.

CHANG, Y. et al. A Hybrid Model for Long-Term Wind Power Forecasting Utilizing NWP Subsequence Correction and Multi-Scale Deep Learning Regression Methods. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, vol. 15, 1, 2024.

DAI, J. FU, L. A wind speed forecasting model using nonlinear auto-regressive model optimized by the hybrid chaos-cloud salp swarm algorithm. Energy, v. 298, 131332, 2024.

DONADIO, L. FANG, J. PORTÉ-AGEL, F. Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast. Energies, v. 14, 338, 2021.

GAO, Y. et al. Multi-step wind speed prediction based on LSSVM combined with ESMD and fractional-order beetle swarm optimization. Energy Reports, v. 9, p. 6114-6134, 2023.

HANIFI, S. et al. Offshore wind power forecasting based on WPD and optimised deep learning methods. Renewable Energy, v. 218, 119241, 2023.

HOU, G. et al. A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation. Renewable Energy, v. 222, 119863, 2024.

HOU, G. WANG, J. FAN, Y. Wind power forecasting method of large-scale wind turbine clusters based on DBSCAN clustering and an enhanced hunter-prey optimization algorithm. Energy Conversion and Management, v. 307, 118341, 2024.

JUNG, C. SCHINDLER, D. Comprehensive validation of 68 wind speed models highlights the benefits of ensemble approaches. Energy Conversion and Management, v. 286, 117012, 2023.

KARIJADI, I. et al. Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method. Renewable Energy, v. 218, 119357, 2023.

LI, G. et al. Hybrid forecasting system considering the influence of seasonal factors under energy sustainable development goals. Measurement, v. 211, 112607, 2023.

LIU, X. et al. A Bayesian Deep Learning-based Wind Power Prediction Model Considering the Whole Process of Blade Icing and De-icing. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v. 20, p 1-11, 2024.

MENG, A. et al. An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division. Energy, v. 299, 131383, 2024.

MIRZA, A. F. et al. Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction. Renewable Energy, v. 220, 119604, 2024.

MORETTIN, P. A.; TOLOI, C. M. C. Modelos de suavização exponencial. Análise de Séries Temporais: Modelos lineares univariados. São Paulo: Edgard Blucher, 2018.

OLIVEIRA, M. S. de. Et al. Integrated data envelopment analysis, multi-criteria decision making, and cluster analysis methods: Trends and perspectives. Decision Analytics Journal, v. 8, 100271, 2023.

PAGANI, R.N. KOVALESKI, J.L. RESENDE, L.M. Methodi Ordinatio: a proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication. Scientometrics, v.105, p. 2109-2135, 2015.

PAGANI, R.N. et al. Methodi Ordinatio 2.0: revisited under statistical estimation, and presenting FInder and RankIn. Quality & Quantity, v. 57, p. 4563-4602, 2023.

PELLEGRINI, F. R.; FOGLIATTO, F. S. Passos para Implantação de Sistemas de Previsão de Demanda - Técnicas e Estudo de Caso. Produção, 11, 1, 2001.

QIAN, Z. et al. A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Applied Energy, v. 235, p. 939-953, 2019.

RIBEIRO, M. H. D. M. Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting. Applied Intelligence, v. 54, p. 3119-3134, 2024.

SHI, X. WANG, J. ZHANG, B. A fuzzy time series forecasting model with both accuracy and interpretability is used to forecast wind power. Applied Energy, v. 335, 122015, 2024.

SOUSA, A. R. D. S. et al. ANÁLISE DE SÉRIES TEMPORAIS. Porto Alegre: SAGAH, 2021.

XIONG, J. et al. A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction. Energy, v. 266, 126419, 2023.

YUZGEC, U. DOKUR, E. BALCIC, M. A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting. Energy, v. 300, 131546, 2024.

WANG, H. et al. A novel multi-layer stacking ensemble wind power prediction model under Tensorflow deep learning framework considering feature enhancement and data hierarchy processing. Energy, v. 286, 129409, 2024.

WANG, J. et al. A novel wind power prediction model improved with feature enhancement and autoregressive error compensation. Journal of Cleaner Production, v. 420, 138386, 2023.

Wang, J. TANG, T. D. JIANG, W. Y. A deterministic and probabilistic hybrid model for wind power forecasting based improved feature screening and optimal Gaussian mixed kernel function. Expert Systems with Applications, v. 251, 123965, 2024.

WU, Z. et al. Explainable temporal dependence in multi-step wind power forecast via decomposition based chain echo state networks. Energy, v. 270, 126906, 2023.

YANG, W. et al. Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China. Journal of Cleaner Production, v. 222, p. 942-959, 2019.

ZHANG, D. et al. A novel spatio-temporal wind speed forecasting method based on the microscale meteorological model and a hybrid deep learning model. Energy, v. 288, 129823, 2024.

ZHANG, Y. et al. Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system. Energy, v. 292, 130492, 2024.

ZHENG, J. et al. A hybrid framework for forecasting power generation of multiple renewable energy sources. Renewable and Sustainable Energy Reviews, v. 172, 110346, 2023.

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Published

2026-07-07

How to Cite

Schrippe, M., Trojan, F., & Schenatto, F. J. A. (2026). Methodi Ordinatio 2.0. Wind fore Hybrid and ensemble models for wind power generation forecasting: a systematic review. Revista Produção Online, 26(2), 5634 . https://doi.org/10.14488/1676-1901.v26i2.5634

Issue

Vol. 26 No. 2 (2026): Fluxo contínuo

Section

Papers

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