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Synthetic wind speed time series generation by dynamic factor model

Author

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  • Cardoso de Mendonça, Mário Jorge
  • Moreira Pessanha, José Francisco
  • Andrade de Almeida, Victor
  • Toscano Medrano, Luiz Alberto
  • Hunt, Julian David
  • Pereira Junior, Amaro Olímpio
  • Nogueira, Erika Carvalho

Abstract

The global transition to renewable energy is driven by the fight against climate change. Wind power plays a crucial role in reducing dependence on fossil fuels and greenhouse gas emissions. Therefore, addressing uncertainties in wind speed variations requires innovative solutions. This study proposes a Bayesian-based approach using a Dynamic Factor Model to generate synthetic monthly average wind speed series. The Dynamic Factor Model framework captures temporal and spatial correlations, improving wind resource representation in operational planning models. The model's autoregressive configuration with common factors, prior distributions, and Bayesian inference techniques enhances predictive capabilities. Validation exercises confirm the model's reliability, accurately capturing seasonal oscillations and spatial correlations across eight wind farms. The study highlights the usefulness of the Dynamic Factor Model in evaluating wind projects and optimizing energy generation strategies, effectively mitigating wind uncertainty, and facilitating renewable energy integration in Brazil's power mix.

Suggested Citation

  • Cardoso de Mendonça, Mário Jorge & Moreira Pessanha, José Francisco & Andrade de Almeida, Victor & Toscano Medrano, Luiz Alberto & Hunt, Julian David & Pereira Junior, Amaro Olímpio & Nogueira, Erika , 2024. "Synthetic wind speed time series generation by dynamic factor model," Renewable Energy, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:renene:v:228:y:2024:i:c:s0960148124006591
    DOI: 10.1016/j.renene.2024.120591
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    References listed on IDEAS

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    1. Paula Medina Maçaira & Yasmin Monteiro Cyrillo & Fernando Luiz Cyrino Oliveira & Reinaldo Castro Souza, 2019. "Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning," Energies, MDPI, vol. 12(5), pages 1-20, March.
    2. Aguilar, Omar & West, Mike, 2000. "Bayesian Dynamic Factor Models and Portfolio Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 338-357, July.
    3. Mário Jorge Mendonça & Luis Alberto Medrano, 2015. "Aplicação do Modelo Fatorial Dinâmico Para Previsão da Receita Tributária no Brasil," Discussion Papers 2064, Instituto de Pesquisa Econômica Aplicada - IPEA.
    4. Ávila, Leandro & Mine, Miriam R.M & Kaviski, Eloy & Detzel, Daniel H.M., 2021. "Evaluation of hydro-wind complementarity in the medium-term planning of electrical power systems by joint simulation of periodic streamflow and wind speed time series: A Brazilian case study," Renewable Energy, Elsevier, vol. 167(C), pages 685-699.
    5. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
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    Cited by:

    1. Hu, Sile & Liu, Dunnan & Yang, Ning & Li, Chenxi & Xu, Erfeng & Xu, Xiaofeng & Wu, Fan, 2025. "AI-based integration of flexible diverse electricity aggregators in multiple electricity markets under uncertainty," Energy, Elsevier, vol. 333(C).
    2. Rafael Araujo Couto & Paula Medina Maçaira Louro & Fernando Luiz Cyrino Oliveira, 2025. "Forecasting Wind Speed Using Climate Variables," Forecasting, MDPI, vol. 7(1), pages 1-23, March.

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