摘要
文章针对现有电力负荷预测方法未能充分捕获电力负荷数据的周期性和趋势性特性导致预测稳定性差的问题,提出了一种短期电力负荷预测模型PowerTimeMixer。首先,基于解耦思想对原始时间序列进行时序分解,并以多尺度方式学习时序的周期和趋势特性;其次,引入卷积下采样机制并通过网络参数共享来匹配循环周期,进一步增强电力负荷数据周期模式的特征提取能力;最后,采用独立的日期模式驱动预测模块,使用多层感知机对输入序列和目标序列时间戳特征进行编码,独立地学习时间戳特征,引导网络根据时间戳特征生成更稳定的预测结果。在电力负荷数据集上的实验结果表明,所提出的方法相比基准模型的预测误差显著降低,具有更稳定的预测性能,从而验证了方法的有效性。
Aiming at the problem that the existing electricity load forecasting methods fail to fully capture electricity load data's periodicity and trend characteristics,resulting in poor prediction stability,this paper proposes a short-term electricity load forecasting model PowerTimeMixer.Firstly,the original time series is decomposed,and the periodicity and trend characteristics of the time series are learned in a multi-scale manner;secondly,the convolution down sampling mechanism is introduced to match the cycle period through network parameter sharing,further enhancing the feature extraction ability of the periodic pattern of electricity load data;finally,an independent date pattern driven forecasting module is introduced,and the multi-layer perceptron is used to encode the timestamp features of the input sequence and the target sequence.The timestamp features are learned independently,guiding the network to generate more stable prediction results according to the timestamp features.The experimental results on the electricity load dataset show that the prediction error of the proposed method is significantly reduced compared with the baseline model,and it has a more stable prediction performance,which verifies the effectiveness of the proposed method.
作者
李裕民
李宏杰
李晓嘉
曹媛媛
谢毅
张超
LI Yumin;LI Hongjie;LI Xiaojia;CAO Yuanyuan;XIE Yi;ZHANG Chao(Shanxi Power Exchange Center,Taiyuan 030000,Shanxi Province,China)
出处
《电网技术》
北大核心
2025年第10期4216-4227,I0058,共13页
Power System Technology
基金
国网山西省电力公司科技项目(52051N230001)。
关键词
电力负荷预测
时序分解
多尺度解耦
日期模式驱动预测
多层感知机
electricity load forecasting
time series decomposition
multi-scale decoupling
data pattern driven forecasts
multilayer perceptron
