首页|强化数据预处理的BLSTNet-CBAM短期电力负荷预测

强化数据预处理的BLSTNet-CBAM短期电力负荷预测

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  • NETL
  • NSTL
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针对负荷数据复杂性、非平稳性以及负荷预测误差较大等问题,提出一种综合特征构建和模型优化的短期电力负荷预测新方法.首先采用最大信息系数(MIC)分析特征变量的相关性,选取与电力负荷序列相关的特征变量,同时,考虑变分模态分解(VMD)方法容易受主观因素的影响,采用霜冰优化算法(RIME)优化VMD,完成原始电力负荷序列的分解.然后改进长短期时间序列网络(LSTNet)作为预测模型,将其递归层LSTM更新为BiLSTM,并引入卷积块注意力机制(CBAM)进行预测.通过对比实验和消融实验的结果表明:经RIME-VMD优化后,LSTM、GRU、LSTNet模型预测的均方根误差(RMSE)均降低 20%以上,显著提高模型预测精度,且能够适应于不同预测模型.所提出的BLSTNet-CBAM模型与LSTM、GRU、LSTNet相比,RMSE分别降低了 35.54%、6.78%、1.46%,提高了短期电力负荷预测的准确性.
Enhanced Data Preprocessing for BLSTNet-CBAM Short-term Power Load Forecasting
A new method for short-term power load forecasting is proposed to address issues such as complex and non-stationary load data,as well as large prediction errors.Firstly,this study utilizes the maximum information coefficient(MIC)to analyze the correlation of feature variables and selects relevant variables related to power load sequences.At the same time,as the variational mode decomposition(VMD)method is susceptible to subjective factors,the study employs the rime optimization algorithm(RIME)to optimize VMD and decompose the original power load sequence.Then,the long and short-term time series network(LSTNet)is improved as the prediction model by replacing the recursive LSTM layer with BiLSTM and incorporating the convolutional block attention mechanism(CBAM).Comparative experiments and ablation experiments demonstrate that RIME-VMD reduces the root mean square error(RMSE)of the LSTM,GRU,and LSTNet models by more than 20%,significantly improving the prediction accuracy of the models,and can be adapted to different prediction models.Compared with LSTM,GRU,and LSTNet,the proposed BLSTNet-CBAM model reduces the RMSE by 35.54%,6.78%,and 1.46%respectively,improving the accuracy of short-term power load forecasting.

short-term power load forecastingrime optimization algorithm(RIME)variational mode decomposition(VMD)long and short-term time series network(LSTNet)convolutional block attention mechanism(CBAM)

陈万志、张思维、王天元

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辽宁工程技术大学软件学院,葫芦岛 125105

国网辽宁省电力有限公司营口供电公司,营口 115002

短期电力负荷预测 霜冰优化算法 变分模态分解 长短期时间序列网络 卷积块注意力机制

国家重点研发计划辽宁省教育厅高等学校科研项目

2018YFB14033032021LJKZ0327

2024

10.15888/j.cnki.csa.009490

计算机系统应用
中国科学院软件研究所

计算机系统应用

CSTPCD
影响因子：0.449
ISSN：1003-3254
年,卷(期)：2024.33(5)
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