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

Li Zixin; Zhang Zhifeng; Yan Jia; Shi Hongtao; Gao Tianji; Ding Maosheng

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

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (4) : 333-340. DOI: 10.19912/j.0254-0096.tynxb.2020-0734

Topics on Key Technologies for Safety of Electrochemical Energy Storage Systems and Echelon Utilization of Decommissioned Power Batteries

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

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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.

Key words

wind power / clustering algorithm / decision tree / confidence interval / neural network / forecasting

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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

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