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

机译：基于稀疏贝叶斯学习和NWP的概率短期风电预测

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摘要

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.
机译：概率短期风电预测对于风电调度的运行和电力系统的可靠性具有重要意义。本文研究了一种基于稀疏贝叶斯学习（SBL）和数值天气预报（NWP）的方法来预测1-24小时范围内的风能概率。在建模过程中，首先，对来自NWP结果的风速数据进行校正，然后使用SBL建立组合数据与发电量之间的关系，以产生概率性发电量预测。此外，在每个模型中，通过使用改进的高斯核函数和通过粒子群优化（PSO）进行参数优化来改进SBL的应用。为了验证所提出的方法，使用了两个真实世界的数据集进行构建和测试。为了进行确定性评估，仿真结果表明，与其他风电预测模型相比，所提出的模型在预测精度上有较大提高。对于概率评估，指标的结果还表明所提出的模型具有出色的性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第12期|785215.1-785215.11|共11页
  • 作者

    Pan Kaikai; Qian Zheng; Chen Niya;

  • 作者单位

    Beihang Univ, Sch Instrument Sci & Optoelect Engn, Beijing 100191, Peoples R China;

    Beihang Univ, Sch Instrument Sci & Optoelect Engn, Beijing 100191, Peoples R China;

    Beihang Univ, Sch Instrument Sci & Optoelect Engn, Beijing 100191, Peoples R China;

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