基于TCN与轻量Autoformer的电力负荷预测  

Power Load Forecasting Based on TCN and Lightweight Autoformer

作　　者：李明[1] 石超山 文贵豪 罗勇航 谭云飞 LI Ming;SHI Chaoshan;WEN Guihao;LUO Yonghang;TAN Yunfei(School of Computer and Information Science,Chongqing Normal University,Chongqing 401331,China)

机构地区：[1]重庆师范大学计算机与信息科学学院,重庆401331

出　　处：《计算机与现代化》2025年第4期6-11,共6页Computer and Modernization

基　　金：国家自然科学基金资助项目(61877051,61170192);重庆市教委项目(113143);重庆市研究生教改重点项目(yjg182022)。

摘　　要：电力负荷预测的精度对节能减排至关重要,更高的精度可以使电力公司做出更合理的规划,提高经济效益。虽然基于Transformer架构改进的Autoformer已经在序列预测任务中取得了不错的结果,但在提取时序特征时没有充分考虑到时间的因果关系,且注意力层中存在过多冗余信息导致模型精度下降和内存浪费。为了解决这些问题,本文提出一种时间卷积网络(TCN)和改进的轻量Autoformer模型相结合的电力负荷预测方法。首先,在Autoformer的编码器中引入时间卷积网络,使得编码器具有更大的感受野并充分考虑样本的因果关系,然后在自相关注意力层之间增加蒸馏机制,减少模型的参数量。最后,在5个公共数据集上的实验结果表明,结合TCN的轻量Autoformer与原始模型相比,MSE指标和MAE指标分别降低了8.95%至32.40%和4.91%至15.51%,且预测效果显著优于其他4种主流方法,显示了其出色的性能。The accuracy of power load forecasting is crucial for energy conservation and emission reduction,and higher accuracy can enable power companies to make more reasonable plans and improve economic benefits.Although Autoformer,based on the improved Transformer architecture,has achieved good results in sequence prediction tasks,it did not fully consider the causal relationship of time when extracting temporal features,and there is too much redundant information in the attention layer,which leads to a decrease in model accuracy and memory consumption.To address these issues,this paper proposes a power load forecasting method that combines Time Convolutional Network(TCN)and an improved lightweight Autoformer model.Firstly,a time convolutional network is introduced into the Autoformer encoder to provide a larger receptive field and fully consider the causal relationship of the samples.Then,a distillation mechanism is added between the autocorrelation attention layers to reduce the number of model parameters.Finally,the results of experiment on five public datasets showed that the lightweight Autoformer combined with TCN reduced MSE and MAE by 8.95%to 32.40%and 4.91%to 15.51%respectively compared to the original model,and the prediction performance is significantly better than the other four mainstream methods,demonstrating its excellent performance.

关 键 词：TRANSFORMER Autoformer 时间卷积网络 注意力蒸馏 负荷预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]