CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12704 > Article
Paper
31 May 2023 Short-term electric power prediction in a certain area based on least squares support vector machine
Xin Xie, Hui Gan
Author Affiliations +
Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 1270402 (2023) https://doi.org/10.1117/12.2680264
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China
Abstract
Short-term power prediction is one of the important tasks in the daily operation of the energy system. With the popularity of smart grid, it brings more difficulties to the accuracy of electricity prediction. The purpose of this study is to explore the characteristics of short-term power data to achieve efficient and accurate power prediction and provide a reference for power grid operation management. Firstly, according to various characteristics of power prediction, this paper builds shortterm power prediction models by using SVM and LS-SVM, respectively. Then, this paper compares the prediction results of the two single models with the test values. It is found that the prediction curve of the LS-SVM model is better than that of the SVM model, and the LS-SVM prediction model has better potential for prediction short-term power consumption. This indicates that different machine learning models are suitable for different types of electrical power predictions.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Xin Xie and Hui Gan "Short-term electric power prediction in a certain area based on least squares support vector machine", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 1270402 (31 May 2023); https://doi.org/10.1117/12.2680264
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 5 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling
Machine learning
Support vector machines
Statistical modeling
Mathematical optimization
Power consumption
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top