Study of the Load Forecasting based on AKDC and LSTM algorithms

As one of the traditional research subjects of power system, load forecasting has always been a hot research direction of related experts and scholars. This paper uses an extended algorithm combining the advantages of adaptive K-means algorithm and distributed clustering algorithm, improves the trad...

Full description

Saved in:
Bibliographic Details
Published inJournal of physics. Conference series Vol. 2589; no. 1; pp. 12035 - 12041
Main Authors Ding, Mingming, Wang, Zhenshu, Zhou, Xinhui, Wang, Kang
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.09.2023
Subjects
Adaptive algorithms
Clustering
Forecasting
Pattern recognition
Physics
Prediction models
Time dependence
Online AccessGet full text
ISSN1742-6588
1742-6596
1742-6596
DOI10.1088/1742-6596/2589/1/012035

Cover

More Information
Summary:As one of the traditional research subjects of power system, load forecasting has always been a hot research direction of related experts and scholars. This paper uses an extended algorithm combining the advantages of adaptive K-means algorithm and distributed clustering algorithm, improves the traditional K-means algorithm, and uses LSTM algorithm to build a load prediction model. LSTMS can learn the advantages of long distance time series dependence to recognize load patterns from the horizontal (time dimension). The simulation results show that the LSTM algorithm based on Adam optimizer improves the accuracy of load prediction, and the proposed algorithm is verified.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1742-6588
1742-6596
1742-6596
DOI:10.1088/1742-6596/2589/1/012035