Prediction of Combination Probability Interval of Wind Power Based on Naive Bayes

YANG Xiyun; ZHANG Yanfeng; YE Tianze; SU Jie

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Prediction of Combination Probability Interval of Wind Power Based on Naive Bayes

更新时间：2025-12-08

- Prediction of Combination Probability Interval of Wind Power Based on Naive Bayes
- Vol. 46, Issue 3, Pages: 1099-1108(2020)
- 作者机构：
  
  华北电力大学控制与计算机工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：31 March 2020，
  
  Published：2020
- 稿件说明：
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YANG Xiyun, ZHANG Yanfeng, YE Tianze, et al. Prediction of Combination Probability Interval of Wind Power Based on Naive Bayes[J]. 2020, 46(3): 1099-1108.
DOI：

YANG Xiyun, ZHANG Yanfeng, YE Tianze, et al. Prediction of Combination Probability Interval of Wind Power Based on Naive Bayes[J]. 2020, 46(3): 1099-1108. DOI：

摘要

为了提高风电功率概率区间预测性能

提出了一种基于朴素贝叶斯的正态指数平滑法和混合滑动核密度估计的组合风电功率区间预测方法。首先

通过朴素贝叶斯分类器建立点预测模型;然后

分别通过正态指数平滑法和混合滑动核密度估计预测误差的概率分布

得出对应的某一置信概率下的预测区间;最后

利用熵权法合理的加权组合正态指数平滑法估计所得预测区间和混合滑动核密度估计所得预测区间

生成最终的风电功率预测区间。研究结果表明:与正态指数平滑法和混合滑动核密度得出的预测区间相比

提出的熵权法加权组合预测可提高区间覆盖率、降低区间平均带宽

证明了该组合概率区间预测方法能同时兼顾可靠性和准确性。论文研究可为风电功率预测提供参考。

Abstract

In order to improve the performance of predicting the probability interval of wind power

a wind power interval prediction method combining normal exponential smoothing and mixed sliding kernel density estimation is proposed. Firstly

the point prediction model is established by the naive bayesian classifier. Then

the probability distribution of the prediction error is estimated by normal exponential smoothing and mixed sliding kernel density estimation

and the corresponding prediction interval under a certain confidence probability is obtained. Finally

the entropy method is used to reasonably combine the prediction interval of normal exponential smoothing and the prediction interval of mixed sliding kernel density estimation to generate the final wind power prediction interval. The results show that

compared with the normal exponential smoothing and mixed sliding kernel density estimation

the proposed combination prediction method combining with an entropy method can be employed to improve the interval coverage and reduce the average bandwidth of intervals

which proves that the method compromises the reliability and accuracy of the prediction. The research can provide a reference for the prediction of wind power.

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