Survey on Short-term Load Forecasting Algorithm Based on Machine Learning
CSTR:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Load forecasting methods emerge one after another to maintain the stability of power grids. However, due to the characteristic difference in the generalization ability of algorithms and model complexity, the applicability of these methods to load forecasting differs. This study discusses and summarizes the research status of short-term power load forecasting both at home and abroad in the past five years from multiple dimensions, such as experimental data sets, data preprocessing, forecasting algorithms, optimization models, and evaluation methods. Meanwhile, we also present a summary of the advantages, disadvantages, and applicability of various forecasting algorithms, and the development trend of the short-term power load forecasting system is expounded and predicted. This study is expected to provide a reference for the forecasting model selection of power system loads in the future.

    Reference
    Related
    Cited by
Get Citation

梁宏涛,刘红菊,李静,王莹,郭超男.基于机器学习的短期负荷预测算法综述.计算机系统应用,2022,31(10):25-35

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:December 28,2021
  • Revised:January 28,2022
  • Adopted:
  • Online: July 07,2022
  • Published:
Article QR Code
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063