居民用电负荷超短期预测研究
DOI:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

TM73

基金项目:


Research on ultra-short-term prediction of residential electricity consumption
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    负荷预测是电力系统安全运行的基础,而由于居民用电负荷的随机性和波动性,可能会影响电力系统的正常运行与维护,因此准确预测居民用电负荷为电网的实时调度提供了有利指导。提出了一种基于长短时记忆型循环神经网络的居民用电负荷超短期预测方法,利用该方法的“记忆”特性挖掘负荷数据间的关联特性,建立了基于基于长短时记忆网络的居民用电负荷超短期预测模型,并和双层前馈神经网络模型仿真结果相对比,其基于长短时记忆网络的预测结果精度更高,验证了模型的有效性。

    Abstract:

    Load forecasting is the basis of safe operation of power system. Due to the randomness and volatility of residential electricity load, it may affect the normal operation and maintenance of power system. Therefore, accurate prediction of residential power load provides favorable guidance for real-time dispatching of power grid. In this paper, an ultra-short-term prediction method for residential electricity load based on long-short-time memory-type cyclic neural network is proposed. The “memory” feature of this method is used to mine the correlation characteristics between load data, and a resident based on long-short-term memory network is established. The ultra-short-term prediction model of electric load is compared with the simulation results of the double-layer feedforward neural network model. The prediction results based on the long-short-time memory network are more accurate, and the validity of the model is verified.

    参考文献
    相似文献
    引证文献
引用本文

林琳,鞠森,于立杰.居民用电负荷超短期预测研究[J].电子测量技术,2019,42(9):98-101

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2021-08-23
  • 出版日期: