Residential load forecasting based on symplectic geometry mode decomposition and GRU neural network with attention mechanism

Author:

Lu Yuting1,Wang Gaocai2,Huang Xianfei1,Wu Man1

Affiliation:

1. School of Electrical Engineering Guangxi University Nanning China

2. School of Computer, Electronics and Information Guangxi University Nanning China

Abstract

AbstractShort‐term residential load forecasting plays an increasingly important role in modern smart grids, with its main challenge being the high volatility and uncertainty of load curves. This article proposes a hybrid Symplectic Geometry Mode Decomposition‐Gated Recurrent Unit with Attention Mechanism (SGMD‐GRUAM) model for hourly residential load forecasting. First, SGMD is used to decompose the residential load and obtain a series of stable subsequences. Then, the Pearson correlation coefficient is used to select features related to each subsequence, such as weather factors. Next, a GRUAM prediction model is constructed for each subsequence. Finally, the final load prediction value is obtained by superimposing the previous component sequences and eliminating the noise sequence. The experiment uses the public dataset from UMass for a case study and compares it with benchmark models such as ARIMA and EEDM‐GRUAM. The experimental results show that the proposed SGMD‐GRUAM model has significant advantages in terms of prediction performance.

Funder

National Natural Science Foundation of China

Publisher

Wiley

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3