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Probabilistic Short-Term Wind Power Forecasting Using Sparse Bayesian Learning and NWP

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  • Kaikai Pan
  • Zheng Qian
  • Niya Chen

Abstract

Probabilistic short-term wind power forecasting is greatly significant for the operation of wind power scheduling and the reliability of power system. In this paper, an approach based on Sparse Bayesian Learning (SBL) and Numerical Weather Prediction (NWP) for probabilistic wind power forecasting in the horizon of 1–24 hours was investigated. In the modeling process, first, the wind speed data from NWP results was corrected, and then the SBL was used to build a relationship between the combined data and the power generation to produce probabilistic power forecasts. Furthermore, in each model, the application of SBL was improved by using modified-Gaussian kernel function and parameters optimization through Particle Swarm Optimization (PSO). To validate the proposed approach, two real-world datasets were used for construction and testing. For deterministic evaluation, the simulation results showed that the proposed model achieves a greater improvement in forecasting accuracy compared with other wind power forecast models. For probabilistic evaluation, the results of indicators also demonstrate that the proposed model has an outstanding performance.

Suggested Citation

  • Kaikai Pan & Zheng Qian & Niya Chen, 2015. "Probabilistic Short-Term Wind Power Forecasting Using Sparse Bayesian Learning and NWP," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, July.
  • Handle: RePEc:hin:jnlmpe:785215
    DOI: 10.1155/2015/785215
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    Cited by:

    1. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    2. Yukun Wang & Aiying Zhao & Xiaoxue Wei & Ranran Li, 2023. "A Novel Ensemble Model Based on an Advanced Optimization Algorithm for Wind Speed Forecasting," Energies, MDPI, vol. 16(14), pages 1-19, July.

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