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基于VMD和双重注意力机制LSTM的短期光伏功率预测
作者:
作者单位:

1.四川大学电气工程学院,四川省成都市 610065;2.国网四川省电力公司,四川省成都市 610041

摘要:

提出了一种基于变分模态分解(VMD)和双重注意力机制长短期记忆(LSTM)的短期光伏功率预测方法。针对光伏功率信号的波动性和非平稳性,利用VMD将光伏功率输出分解为不同频率的分量,使用LSTM对各分量进行预测,并在LSTM基础上引入特征和时序双重注意力机制。为自主挖掘光伏功率输出与各气象特征之间的关联关系,避免传统方法依赖于专家经验关联规则阈值的限制,引入特征注意力机制实时计算各气象特征量的贡献率,并对特征权重进行修正;同时,为挖掘当前时刻光伏功率输出与历史时序信息之间的关联关系,引入时序注意力机制自主提取历史关键时刻点信息,提高长时间序列预测效果的稳定性。基于中国西南某实际光伏发电站数据进行预测实验,并与其他方法进行对比,验证了该方法的有效性。

关键词:

基金项目:

国家重点研发计划资助项目(2018YFB0905200)。

通信作者:

作者简介:

杨晶显(1984—),女,博士研究生,主要研究方向:新能源预测、电力系统运行与规划。E-mail:yangjxian12@163.com
张帅(1987—),男,博士研究生,主要研究方向:电力系统运行与控制、电力市场。E-mail:shuaizhang@stu.csu.edu.cn
刘继春(1975—),男,通信作者,教授,博士生导师,IEEE高级会员,主要研究方向:电力系统数据挖掘、稳定与控制,电力市场。E-mail:jichunliu@scu.edu.cn


Short-term Photovoltaic Power Prediction Based on Variational Mode Decomposition and Long Short-term Memory with Dual-stage Attention Mechanism
Author:
Affiliation:

1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China;2.State Grid Sichuan Electric Power Company, Chengdu 610041, China

Abstract:

This paper proposes a short-term photovoltaic (PV) power prediction method based on variational mode decomposition (VMD) and long short-term memory (LSTM) with dual-stage attention mechanism. Aiming at the fluctuation and instability of PV power signals, VMD is used to decompose the PV power output into different components with different frequencies. LSTM is used to predict the individual components. A dual-stage attention mechanism including the feature attention mechanism and temporal attention mechanism is adopted based on LSTM. For expressing the association between PV power output and various meteorological characteristics and avoiding the limitation of the threshold relying on the expert experience in the traditional method, the feature attention mechanism is introduced to calculate the contribution rate of different meteorological characteristic variables and modify feature weights in real time. Meanwhile, in order to obtain the association between the PV power outputs at the current time and the historical time series, the temporal attention mechanism is introduced to automatically extract historical key time point information, which improves the stability of prediction performance of long-term sequences. The prediction experiment is carried out based on the data of an actual PV power station in Southwest China. The effectiveness of the proposed algorithm is verified by the comparison with other methods.

Keywords:

Foundation:
This work is supported by National Key R&D Program of China (No. 2018YFB0905200).
引用本文
[1]杨晶显,张帅,刘继春,等.基于VMD和双重注意力机制LSTM的短期光伏功率预测[J].电力系统自动化,2021,45(3):174-182. DOI:10.7500/AEPS20200226011.
YANG Jingxian, ZHANG Shuai, LIU Jichun, et al. Short-term Photovoltaic Power Prediction Based on Variational Mode Decomposition and Long Short-term Memory with Dual-stage Attention Mechanism[J]. Automation of Electric Power Systems, 2021, 45(3):174-182. DOI:10.7500/AEPS20200226011.
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  • 收稿日期:2020-02-26
  • 最后修改日期:2020-08-19
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  • 在线发布日期: 2021-02-03
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