基于生成对抗网络和EMD-ISSA-LSTM的短期电力负荷预测
Short-term power load forecasting based on generative adversarial networks and EMD-ISSA-LSTM
曾进辉 1苏旨音 1肖锋 1刘颉 1孙贤水1
作者信息
- 1. 湖南工业大学电气与信息工程学院 株洲 412007
- 折叠
摘要
针对电力负荷本身固有的不稳定性和非线性,导致短期电力负荷预测精度难以提升问题.本文提出一种基于EMD和LSTM相结合的短期电力负荷预测方法.首先,利用EMD将原始信号分解为一系列本征模态函数和一个残差量.随后,将所有分量输入LSTM中.为进一步提升负荷预测精度和优化模型泛化能力,分别对大分量信号引入改进麻雀搜寻算法优化LSTM超参数和对原始负荷数据引入表格生成对抗网络生成新数据样本,形成基于表格生成对抗网络和EMD-ISSA-LSTM的短期电力负荷预测方法.最后,分别采用第九届电工数学建模竞赛负荷数据和湖南省某地市含分布式电源的负荷数据进行效果验证.结果表明,在两种数据集下,该模型的平均绝对百分比误差分别为2.37%和2.76%,验证了该方法的有效性.
Abstract
Aiming at the inherent instability and nonlinearity of power load,which makes it difficult to improve the accuracy of short-term power load prediction.In this paper,we propose a short-term power load prediction method based on the combination of EMD and LSTM.First,the original signal is decomposed into a series of eigenmode functions and a residual quantity using EMD.Subsequently,all the components are input into the LSTM.To further improve the accuracy of load forecasting and optimize the generalization ability of the model,an improved sparrow search algorithm is introduced to optimize LSTM hyperparameters for large component signals,and a table generative adversarial network is introduced to generate new data samples for raw load data,forming a short-term power load forecasting method based on table generative adversarial network and EMD-ISSA-LSTM.Finally,the load data of the ninth mathematical modeling competition for electricians and the load data of a prefecture and city in Hunan Province containing distributed power sources are used to validate the effect,and the results show that the mean absolute percentage error of the model under the two datasets is 2.37%and 2.26%,respectively.The validity of the method is verified.
关键词
短期电力负荷预测/经验模态分解/长短期记忆神经网络/改进麻雀搜寻算法/生成对抗网络Key words
short-term power load forecasting/modal decomposition/long short-term memory neural networks/improved sparrow search algorithm/generative adversarial network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据