基于趋势聚类与决策树的风电功率多区间复合短期预测方法

李梓鑫; 张智峰; 闫佳; 师洪涛; 高天霁; 丁茂生

doi:10.19912/j.0254-0096.tynxb.2020-0734

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (4) : 333-340. DOI: 10.19912/j.0254-0096.tynxb.2020-0734

电化学储能安全性与退役动力电池梯次利用关键技术专题

基于趋势聚类与决策树的风电功率多区间复合短期预测方法

师洪涛¹, 高天霁¹, 丁茂生^1,2, 李梓鑫¹, 张智峰¹, 闫佳¹

作者信息 +

WIND POWER MULTI-INTERVAL COMPOSITE SHORT-TERM PREDICTION METHOD BASED ONTREND CLUSTERING AND DECISION TREE

Shi Hongtao¹, Gao Tianji¹, Ding Maosheng^1,2, Li Zixin¹, Zhang Zhifeng¹, Yan Jia¹

Author information +

文章历史 +

摘要

首先对如何提取风电功率的趋势特征以及建立合适的短期预测模型进行分析,进而对不同功率趋势下的预测精度展开研究。为了提高在多区间下的复合预测性能,提出一种基于趋势聚类与决策树的风电功率多区间复合短期预测方法,即在不同的功率趋势分类中,进行不同的概率估计,然后将分类估计数值复合为完整的估计结果,提高了在多复合区间条件下预测结果的覆盖率,且通过算法模型的优化保证预测速度和性能。以上研究均采用算例验证了所提方法的有效性。

Abstract

In this paper, the methods how to extract the trend characteristics of wind power and develop short term forescasting model are analyzed firstly. The study about the prediction accuracy improved under different power trends is carried out. Then, in order to improve the performance of composite prediction in multi-interval, a method based on trend clustering and decision tree for multi-interval composite wind power short-term prediction is proposed. Namely in the classification of different power trend, different probability estimates are carried out, and then the classified estimation values are compounded into the complete estimation results, in which the coverage of the prediction results under the condition of multi-compound interval is improved. Meanwhile, the algorithm model is optimized to ensure the prediction speed and performance. Finally, examples are used to verify the validity of the proposed method.

导出引用

师洪涛, 高天霁, 丁茂生, 李梓鑫, 张智峰, 闫佳. 基于趋势聚类与决策树的风电功率多区间复合短期预测方法[J]. 太阳能学报. 2022, 43(4): 333-340 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0734

Shi Hongtao, Gao Tianji, Ding Maosheng, Li Zixin, Zhang Zhifeng, Yan Jia. WIND POWER MULTI-INTERVAL COMPOSITE SHORT-TERM PREDICTION METHOD BASED ONTREND CLUSTERING AND DECISION TREE[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 333-340 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0734

中图分类号： TM614

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