IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v228y2024ics0960148124006591.html
   My bibliography  Save this article

Synthetic wind speed time series generation by dynamic factor model

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124006591
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.120591?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marko Hočevar & Lovrenc Novak & Primož Drešar & Gašper Rak, 2022. "The Status Quo and Future of Hydropower in Slovenia," Energies, MDPI, vol. 15(19), pages 1-13, September.
    2. Vilkkumaa, Eeva & Liesiö, Juuso & Salo, Ahti, 2014. "Optimal strategies for selecting project portfolios using uncertain value estimates," European Journal of Operational Research, Elsevier, vol. 233(3), pages 772-783.
    3. Gruber, Lutz F. & West, Mike, 2017. "Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models," Econometrics and Statistics, Elsevier, vol. 3(C), pages 3-22.
    4. Lukas Kriechbaum & Philipp Gradl & Romeo Reichenhauser & Thomas Kienberger, 2020. "Modelling Grid Constraints in a Multi-Energy Municipal Energy System Using Cumulative Exergy Consumption Minimisation," Energies, MDPI, vol. 13(15), pages 1-23, July.
    5. Steinegger, Josef & Hammer, Andreas & Wallner, Stefan & Kienberger, Thomas, 2024. "Revolutionizing heat distribution: A method for harnessing industrial waste heat with supra-regional district heating networks," Applied Energy, Elsevier, vol. 372(C).
    6. Behrang Shirizadeh, Quentin Perrier, and Philippe Quirion, 2022. "How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    7. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    8. Liu, Hailiang & Andresen, Gorm Bruun & Greiner, Martin, 2018. "Cost-optimal design of a simplified highly renewable Chinese electricity network," Energy, Elsevier, vol. 147(C), pages 534-546.
    9. Géremi Gilson Dranka & Paula Ferreira, 2020. "Electric Vehicles and Biofuels Synergies in the Brazilian Energy System," Energies, MDPI, vol. 13(17), pages 1-22, August.
    10. Shirizadeh, Behrang & Quirion, Philippe, 2022. "The importance of renewable gas in achieving carbon-neutrality: Insights from an energy system optimization model," Energy, Elsevier, vol. 255(C).
    11. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Kiel Working Papers 1799, Kiel Institute for the World Economy (IfW Kiel).
    12. Katalin Varga & Tibor Szendrei, 2024. "Non-stationary Financial Risk Factors and Macroeconomic Vulnerability for the UK," Papers 2404.01451, arXiv.org.
    13. Conti, Gabriella & Frühwirth-Schnatter, Sylvia & Heckman, James J. & Piatek, Rémi, 2014. "Bayesian exploratory factor analysis," Journal of Econometrics, Elsevier, vol. 183(1), pages 31-57.
    14. Niko Hauzenberger & Maximilian Bock & Michael Pfarrhofer & Anna Stelzer & Gregor Zens, 2018. "Implications of macroeconomic volatility in the Euro area," Papers 1801.02925, arXiv.org, revised Jun 2018.
    15. Antonello Loddo & Shawn Ni & Dongchu Sun, 2011. "Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 342-355, July.
    16. Gorre, Jachin & Ortloff, Felix & van Leeuwen, Charlotte, 2019. "Production costs for synthetic methane in 2030 and 2050 of an optimized Power-to-Gas plant with intermediate hydrogen storage," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    17. Christoph Streuling & Johannes Pagenkopf & Moritz Schenker & Kim Lakeit, 2021. "Techno-Economic Assessment of Battery Electric Trains and Recharging Infrastructure Alternatives Integrating Adjacent Renewable Energy Sources," Sustainability, MDPI, vol. 13(15), pages 1-30, July.
    18. Shirizadeh, Behrang & Quirion, Philippe, 2021. "Low-carbon options for the French power sector: What role for renewables, nuclear energy and carbon capture and storage?," Energy Economics, Elsevier, vol. 95(C).
    19. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
    20. A. G. Olabi & Khaled Obaideen & Mohammad Ali Abdelkareem & Maryam Nooman AlMallahi & Nabila Shehata & Abdul Hai Alami & Ayman Mdallal & Asma Ali Murah Hassan & Enas Taha Sayed, 2023. "Wind Energy Contribution to the Sustainable Development Goals: Case Study on London Array," Sustainability, MDPI, vol. 15(5), pages 1-22, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:228:y:2024:i:c:s0960148124006591. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.