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Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

机译：基于混合人工神经网络的概率风电功率预测

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摘要

The uncertainty of wind power generation imposes significant challenges to optimal operation and control of electricity networks with increasing wind power penetration. To effectively address the uncertainties in wind power forecasts, probabilistic forecasts that can quantify the associated probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications.
机译：随着风力渗透的增加，风力发电的不确定性对电网的最佳运行和控制提出了重大挑战。为了有效解决风电预测中的不确定性，可以量化相关的预测误差概率的概率预测提供了一种替代的有效解决方案。本文提出了一种混合人工神经网络方法来生成风电的预测间隔。极限学习机被用于进行风能的点预测并通过自举技术估计模型的不确定性。随后，采用最大似然估计方法构造一个独特的神经网络，以估计预测结果的噪声方差。使用来自丹麦的实际风能数据，对高分辨率（10分钟）风能的多步预测进行了测试。数值结果表明，所提出的混合人工神经网络方法对风电概率的预测是有效和高效的，在实际应用中具有很高的潜力。

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  • 来源
    《Electric Power Components and Systems》 |2016年第15期|1656-1668|共13页
  • 作者

    Can Wan; Yonghua Song; Zhao Xu; Guangya Yang; Arne Hejde Nielsen;

  • 作者单位

    Department of Electrical Engineering, Tsinghua University, Beijing 100084, China ,Department of Electrical Engineering, The Hong Kong Polytechnic University, Hung Horn, Hong Kong ,Centre for Electric Power and Energy, Technical University of Denmark, Kgs. Lyngby, Denmark;

    Department of Electrical Engineering, Tsinghua University, Beijing, China ,College of Electrical Engineering, Zhejiang University, Hangzhou, China;

    Department of Electrical Engineering, The Hong Kong Polytechnic University, Hung Horn, Hong Kong;

    Centre for Electric Power and Energy, Technical University of Denmark, Kgs. Lyngby, Denmark;

    Centre for Electric Power and Energy, Technical University of Denmark, Kgs. Lyngby, Denmark;

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  • 正文语种 eng
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  • 关键词

    wind power; forecasting; prediction intervals; artificial neural networks; extreme learning machine; maximum likelihood estimation;

    机译：风力;预测;预测间隔;人工神经网络;极限学习机;最大似然估计;

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