基于朴素贝叶斯的风电功率组合概率区间预测  

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

作　　者：杨锡运[1] 张艳峰 叶天泽 苏杰[2] YANG Xiyun;ZHANG Yanfeng;YE Tianze;SU Jie(Department of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China;Department of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学控制与计算机工程学院,北京102206 [2]华北电力大学控制与计算机工程学院,保定071003

出　　处：《高电压技术》2020年第3期1099-1108,共10页High Voltage Engineering

基　　金：国家自然科学基金(51677067);中央高校基本科研业务费专项资金(2018MS27).

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。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.

关 键 词：风电功率 区间预测 朴素贝叶斯 指数平滑法 核密度估计 熵权法 

分 类 号：TM614[电气工程—电力系统及自动化]