首页|基于QMD-HBiGRU的短期光伏功率预测方法

基于QMD-HBiGRU的短期光伏功率预测方法

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为了解决光伏功率数据固有的强不确定性导致单一预测模型预测精度不高的问题,提出一种基于二次模态分解和混合双向门控循环单元模型(hybrid bi-directional gated recurrent unit,HBiGRU)的短期光伏功率预测方法.首先,为应对光伏功率数据的不确定性,基于自适应噪声完备集合经验模态分解、样本熵和变分模态分解对光伏功率数据进行处理,得到一系列较为平稳的本征模函数分量;其次,构建HBiGRU模型以充分挖掘各分量与光伏功率影响因素之间的特征关系,得到各分量预测结果;最后,将各分量预测结果叠加得到短期光伏功率预测结果.以澳大利亚某地光伏电站数据进行测试,仿真结果表明:所提集成预测模型能够有效提高短期光伏功率预测精度,与其他预测模型相比,其归一化平均绝对误差和均方根误差分别降低了3.21%和5.04%,决定系数提高了22.7%.
Short-term PV Forecasting Method Based on the QMD-HBiGRU Model
In order to solve the problem that the inherent strong uncertainty in PV power data leads to the low prediction accuracy of a single prediction model,a short-term PV power prediction method based on quadratic modal decomposition and hybrid bi-directional gated recurrent unit(HBiGRU)model is proposed.Firstly,to cope with the uncertainty of PV power data,a series of smoother eigenfunction components are obtained by processing PV power data based on adaptive noise-complete ensemble empirical modal decomposition,sample entropy and variational modal decomposition.Secondly,the HBiGRU model is constructed to fully explore the characteristic relationship between each component and PV power influencing factors,and the prediction results of each component are obtained.Finally,the prediction results of each component are superimposed to obtain the short-term PV power prediction results.The simulation results show that the proposed integrated prediction model can be adopted to effectively improve the short-term PV power prediction accuracy.Compared with the results from other prediction models,the normalized mean absolute error and root mean square error are reduced by 3.21%and 5.04%,respectively,and the coefficient of determination is improved by 22.7%.

short term PV power forecasthybrid bi-directional gated circulation unitcomplete ensemble empirical mode decomposition with adaptive noisevariational mode decompositionquadratic mode decompositiondeep learning

吉兴全、赵国航、叶平峰、孟祥剑、杨明、张玉敏

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山东科技大学电气与自动化工程学院,青岛 266590

山东科技大学储能技术学院,青岛 266590

山东大学电网智能化调度与控制教育部重点实验室,济南 250061

短期光伏功率预测 混合双向门控循环单元 自适应噪声完备集合经验模态分解 变分模态分解 二次模态分解 深度学习

国家自然科学基金山东省自然科学基金山东省自然科学基金

52107111ZR2022ME219ZR2021QE117

2024

10.13336/j.1003-6520.hve.20230333

高电压技术
中国电力科学研究院　中国电机工程学会

高电压技术

CSTPCD北大核心
影响因子：2.32
ISSN：1003-6520
年,卷(期)：2024.50(9)
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