基于优化VMD与TCN-ISE-Pyraformer的短期电力负荷预测

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关键词：短期电力负荷预测;时序卷积网络;Pyraformer;ISE模块;变分模态分解;麻雀搜索算法;金字塔注意力模型中图分类号：TM715 文献标志码：A doi：10.12415/j.issn.1671-7872.25006

Short-term Power Load Forecasting Based on Optimized VMD and TCN-ISE-Pyraformer

ZHANG Xuan， LI Hongyue

（School ofElectricaland Information Enginering，Anhui UniversityofScience and Technology，Huainan 232001， China）

Abstract： Aiming at the problem that traditional prediction models struggle to simultaneously capture global and local features of multifeature load data，a hybrid prediction model based on sparrow search algorithm （SSA）- optimized variational mode decomposition （VMD）and improved squeeze and excitation （ISE） module，and temporal convolutional network （TCN） and Pyraformer was proposed.First， the SSA was employed to optimize VMD parameters， decomposing the highly oscilatory dynamic load sequence into multiple stationary modal components， thereby reducingthe non-stationarity of the original data.Then，the obtained intrinsic mode functions was input into the TCN model to capture the local features of the data，while the ISE module adaptively assgned appropriate weights to the extracted features，thereby reducing the impact of redundant information on the prediction results. Finally，the weighted data was fed into the Pyraformer model to capture the global features and generated thefinal prediction results.To validate the mode's performance，real-world power load datasets from two regions were used for simulation experiments.Theresults show that in both cases，the proposed model achieves the coefficients of determination is O.9949and0.9842，respectively，outperformingother comparativemodels.This verifies the proposed model'ssuperiority in simultaneously captureglobal and local features of multifeature load data， demonstrating higher prediction accuracy and stability.

：ywords：short-term power load forecasting; temporal convolutional network; Pyraformer; ISE module; variational mode decomposition; sparrow search algorithm; pyramidal attention model

电能作为现代社会发展的基石，深刻影响着生产与生活的方方面面，是经济增长、民生改善和社会稳定的重要保障。（剩余17439字）