skip to main content
10.1145/3674225.3674272acmotherconferencesArticle/Chapter ViewAbstractPublication PagespeaiConference Proceedingsconference-collections
research-article

Research on Short-term Load Forecasting of Power System Based on Deep Learning

Published: 31 July 2024 Publication History

Abstract

As a key supporting work that cannot be ignored, power load forecasting is an important prerequisite for ensuring the safe, reliable and stable operation of the power system. Using deep learning methods to learn features of power load data is expected to improve the accuracy of short-term power load forecasting. This article completed the construction of three different input and output models based on the multi-factor BIGRU network. The model is experimentally simulated using load data sets from the National Electricity Market. Experimental results show that compared with the prediction effects of MLR, SVR, LSTM, GRU and other models that consider multiple factors, the prediction effect of the BIGRU model that considers multiple factors is significantly better.

References

[1]
L. Liang, Y. Z. Ma. 2023. Power system harmonic energy measurement error correction method based on deep learning. Automation Technology and Application, 42(09): 49-52+57.
[2]
Z. F. Ren. 2023. Research on power system dispatching based on deep learning. Electrical Technology and Economics, (06):60-63.
[3]
H. B. Chen. 2023. Research on intelligent capture of power system abnormal data under the Caffe deep learning framework. Wireless Internet Technology, 20(09): 117-119.
[4]
T. Y. Yang, X. J. Sun. 2023. An Overview of Target Detection Algorithms for Faults of Power Transmission Lines Based on Deep Learning. Journal of Chongqing Electric Power College, 28(01): 1-4+23.
[5]
L. Miao, Q. Li, Y. Jiang, 2023. Application of deep learning in power system prediction. Journal of Engineering Science, 45(04): 663-672.
[6]
L. Guan, J. Y. Huang, Q. H. Cai, etc. 2022. Application and Prospect of Graph Deep Learning Technique in Power System Analysis and Decision. High Voltage Technology, 48(09): 3405-3422.
[7]
Y. B. Wang, J. Y. Wu, J. S. Ji, 2023. Integrated Assessment of Power System Transient Frequency Security Based on Deep Residual Shrinkage Network. Power Grid Technology, 47(02): 482-494.
[8]
L. Cheng, Y. Tang, X. L. Du. 2022. Power Systems Enhanced by Deep Reinforcement Learning, (03):66-67+80.
[9]
Z. G. Shao, C. S. Zhang, F. X. Chen, etc. 2023. A review of generative adversarial networks and their applications in power systems. Chinese Journal of Electrical Engineering, 43(03):987-1004.
[10]
H. X. Zang, J. W. Guo, M. Y. Huang, etc. 2021. Power system state estimation under time-varying topology based on deep transfer learning. Power System Automation, 245(24): 49-56.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
January 2024
969 pages
ISBN:9798400716638
DOI:10.1145/3674225
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 July 2024

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

PEAI 2024

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 16
    Total Downloads
  • Downloads (Last 12 months)16
  • Downloads (Last 6 weeks)1
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media