计算机工程2025，Vol.51Issue(10)：369-380,12.DOI:10.19678/j.issn.1000-3428.0070067
基于动态多尺度与双重注意力的短期电力负荷预测
Short-term Power Load Forecasting Based on Dynamic Multi-Scale and Dual Attention Mechanisms
摘要
Abstract
Short-term power load forecasting plays a crucial role in the optimal scheduling and safe operation of power systems.Power load data exhibit multiperiod characteristics,showing different patterns and trends at various time scales.Accurately extracting the scale size helps identify and separate these features.Current methods use a fixed patch length or a set of fixed patch lengths as steps and encode time series into segments called patches.However,these methods cannot adapt to the complex dynamic changes in real-world load series data.Therefore,this paper proposes a prediction model based on a dynamic Multi-scale and Dual Attention Transformer(MDAT).First,Successive Variational Mode Decomposition(SVMD)is used to separate different time patterns in the load series,and Fast Fourier Transform(FFT)is performed to extract the significant period of each pattern.Subsequently,based on the detected significant periods,the load series is divided into different time resolutions using patches of varying sizes,and multiple branches of a transformer are used to simultaneously model the dependencies of the sequences segmented at different scales.Next,dual attention is applied to these patches to capture the global correlations and local details.Finally,nonlinear feature fusion is performed on the outputs of each branch,and the final load prediction results are obtained by stacking multiple transformer modules.Experimental results on two public datasets demonstrate that the proposed model performs well in terms of prediction accuracy.Compared to the latest models based on Transformer and Multilayer Perceptron(MLP),the Mean Absolute Error(MAE)on the Australia and Morocco datasets is reduced by 10.26％-17.06％and 9.08％-70.25％,respectively.关键词
短期负荷预测/逐次变分模态分解/多尺度特征/双重注意力/Transformer模块Key words
short-term load forecasting/Successive Variational Mode Decomposition(SVMD)/multi-scale features/dual attention/Transformer module分类
计算机与自动化引用本文复制引用
朱莉,高靖凯,朱春强,邓凡..基于动态多尺度与双重注意力的短期电力负荷预测[J].计算机工程,2025,51(10):369-380,12.基金项目
国网陕西省电力有限公司科技项目(5226PX240003) （5226PX240003）
国网陕西省电力有限公司数字化项目(B326PX230001,B326PX230000) （B326PX230001,B326PX230000）
陕西省自然科学基础研究项目(2022JM317). （2022JM317）
