现代电力2025，Vol.42Issue(5)：891-900,10.DOI:10.19725/j.cnki.1007-2322.2023.0250
基于变分非线性调频模态分解及TCN-TPA-LSTM的短期电力负荷组合预测模型
Short-term Load Combination Forecasting Model Based on Variational Nonlinear FM Mode Decomposition and TCN-TPA-LSTM
摘要
Abstract
The"double-high and double-peak"characteristics of power loads are becoming increasingly prominent with the advancement of new power system,necessitating reliable and accurate load forecasting for power system operation planning.To predict the power load with better accuracy,a short-term power load combination prediction model based on MIC-VNCMD-TCN-TPA-LSTM is proposed.The maximal information coefficient(MIC)theory is utilized to analyze the nonlinear coupling of load and meteorological information and identify the crucial information.Variational nonlinear chirp mode decomposition(VNCMD)is introduced to process the nonlinear non-stationary load data and decompose them into corresponding components.On this basis,a combined TCN-TPA-LSTM prediction model is constructed,and the corresponding prediction model is selected according to the prediction evaluation index of each element.Subsequently,the overall prediction results are reorganized.The actual electric load data from certain place is used as the dataset for comparison experiments,which demonstrates superior accuracy and generalization capability compared to other prediction models,thus verifying the effectiveness and superiority of the proposed method.关键词
短期电力负荷预测/最大信息系数/变分非线性调频模态分解/时间卷积网络/时序模式注意力机制/长短期记忆网络Key words
short-term power load forecasting/maximal information coefficient/variational nonlinear FM mode decomposition/temporal convolutional network/temporal pattern attention mechanism/long short-term memory network分类
信息技术与安全科学引用本文复制引用
王博宇,文中,周翔,赵迪,闫文文,覃治银..基于变分非线性调频模态分解及TCN-TPA-LSTM的短期电力负荷组合预测模型[J].现代电力,2025,42(5):891-900,10.基金项目
国家自然科学基金项目(51807110).Project Supported by National Natural Science Foundation of China(51807110). （51807110）
