Very short-term probabilistic forecasting of wind power based on OKDE

被引：0

作者：

Wang, Sen ^{[1
]}

Sun, Yonghui ^{[1
]}

Chen, Li ^{[1
]}

Wu, Pengpeng ^{[1
]}

Zhou, Wei ^{[1
]}

Yuan, Chang ^{[1
]}

机构：

[1] Hohai Univ, Coll Energy & Elect Engn, Nanjing 210098, Peoples R China

来源：

2022 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | 2022年

基金：

国家重点研发计划;

关键词：

WP; deterministic forecasting; probabilistic forecasting; CEEMD; LSTM; OKDE;

D O I：

10.1109/SSCI51031.2022.10022201

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Accurate wind power (WP) forecasting plays an important role in the safe and stable operation of power systems, in the context of a high proportion of WP. In this paper, a two-stage WP very short-term probabilistic forecasting model is proposed. Firstly, the different frequency components are obtained based on complementary ensemble empirical mode decomposition (CEEMD) of historical data. Secondly, a long short-term memory (LSTM) based WP very short-term deterministic forecasting model was developed. An optimized kernel density estimate (OKDE) is established to fit the errors by means of band width (BW) constraints, and the quantile is calculated at different confidence levels. Finally, the deterministic forecasting results are combined with the quantile to calculate the probabilistic forecasting interval. Case Studies are also carried out to verify the effectiveness of the model proposed in this paper.

引用

页码：1108 / 1112

页数：5

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