Probability Prediction of Ultra-short-term Wind Power Considering Unbalanced Distribution of Training Samples

被引：0
|
作者：
Li D. [1 ]
Fang Z. [1 ]
Miao S. [2 ]
Hu Y. [1 ,2 ]
Liang Y. [2 ,3 ]
He S. [2 ,3 ]
机构：
[1] Electric and New Energy Faculty, China Three Gorges University, Hubei Province, Yichang
[2] Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Hubei Province, Yichang
[3] Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, Hubei Province, Yichang
来源：
Dianwang Jishu/Power System Technology | 2024年 / 48卷 / 03期
基金：
中国国家自然科学基金;
关键词：
deep belief network; feature distribution smoothing; label distribution smoothing; mixed density network; unbalanced distribution of training samples; wind power probability prediction;
D O I：
10.13335/j.1000-3673.pst.2023.1303
中图分类号：
学科分类号：
摘要：
A probability prediction method of ultra-short-term wind power is proposed considering the unbalanced distribution of training samples. Firstly, a deep belief mixture density network is built. The hidden features of input variables are extracted through the deep belief network’s unique pre-training and fine-tuning mechanism. A bounded Beta-mixed PDF is used to accurately characterize the probability distribution of forecasting wind power and realize the nonlinear mapping from hidden features to PDF parameters. Then, a distribution smoothing strategy of training samples is introduced to mitigate the negative consequences of an unbalanced distribution of training samples from both input and output distribution aspects. The feature distribution smoothing technology is used to correct the input features, and the label distribution smoothing technology is employed to assign different weights to sample errors. The actual case results show that compared with the popular probability prediction models, the proposed model can achieve higher accuracy in both point and probability prediction, especially in low-density sample areas. © 2024 Power System Technology Press. All rights reserved.
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页码：1133 / 1145
页数：12
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