Short-term power load forecasting based on improved Autoformer model

被引：0

作者：

Fan X. ^{[1
]}

Li Y. ^{[1
]}

机构：

[1] College of Control and Computer Engineering, North China Electric Power University, Beijing

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2024年 / 44卷 / 04期

关键词：

Nystrom self-Attention mechanism; Sdformer model; short-term power load forecasting; timing decomposition module;

D O I：

10.16081/j.epae.202305011

中图分类号：

学科分类号：

摘要：

Aiming at the low accuracy problem of short-term power load forecasting caused by the influence of multiple uncertain factors such as weather, temperature and holiday, a short-term power load forecasting model based on an improved Autoformer model is proposed. By changing the pre-processing convention of sequence decomposition, an internal decomposition module of depth model is designed, which extracts the intrinsically complex time series trend of hidden state in the model, and makes the model have the ability to decompose complex time series asymptotically. The Nystrom self-Attention mechanism is proposed, which uses the Nystrom method to approximate the standard self-Attention mechanism. The experimental results of power load forecasting in a region show that the proposed model has lower time complexity and higher accuracy than the standard Autoformer model. © 2024 Electric Power Automation Equipment Press. All rights reserved.

引用

页码：171 / 177

页数：6

共 17 条

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