LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization

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成果类型：

期刊论文

作者：

Yang, Tongguang;Li, Bin;Xun, Qian*

通讯作者：

Xun, Qian

作者机构：

[Li, Bin; Yang, Tongguang] Hunan City Univ, Coll Mechan & Elect Engn, Yiyang 413000, Peoples R China.

[Xun, Qian] Chalmers Univ Technol, Dept Elect Engn, S-41279 Gothenburg, Sweden.

通讯机构：

[Xun, Qian] C

Chalmers Univ Technol, Dept Elect Engn, S-41279 Gothenburg, Sweden.

语种：

英文

关键词：

Bayesian optimization;deep learning;features extraction;LSTM-attention-embedding model;residual connection

期刊：

IEEE ACCESS

ISSN：

2169-3536

年：

2019

卷：

页码：

171471-171484

DOI：

10.1109/ACCESS.2019.2954290

基金类别：

This work was supported in part by the National Nature Science Foundation for China under Grant 61321003 and Grant 61290325, in part by the Zhejiang Natural Science Foundation under Grant LY19F030002, and in part by the Huzhou Public Welfare Application Research Project under Grant 2019GZ02.

机构署名：

本校为第一机构

院系归属：

机械与电气工程学院

摘要：

Photovoltaic (PV) output is susceptible to meteorological factors, resulting in intermittency and randomness of power generation. Accurate prediction of PV power output can not only reduce the impact of PV power generation on the grid but also provide a reference for grid dispatching. Therefore, this paper proposes an LSTM-attention-embedding model based on Bayesian optimization to predict the day-ahead PV power output. The statistical features at multiple time scales, combined features, time features and wind speed categorical features are exp...

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LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization

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