CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12788 > Article
Paper
25 September 2023 Smart energy-load matching strategy for distribution networks based on deep learning and K-means clustering algorithm
Lingyu Liang, Wenqi Huang, Shang Cao, Huanming Zhang, Xiangyu Zhao, Hanju Li
Author Affiliations +
Proceedings Volume 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023); 1278864 (2023) https://doi.org/10.1117/12.3004608
Event: Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 2023, Kuala Lumpur, Malaysia
Abstract
The traditional energy-saving and load matching strategies for distribution networks have the problem of low accuracy in predicting the capacity of power equipment. Therefore, a new intelligent energy load matching strategy is proposed, which uses deep learning algorithms and K-means clustering algorithms to process and standardize power data, extract data features, and construct a capacity prediction model for energy storage devices in distribution networks. By finetuning the model structure network, the load condition of the distribution device is predicted, and the dynamic matching of source and load is achieved. Experimental verification shows that the matching effect of this strategy is superior to traditional methods, with a significant reduction in unit output and a high source load matching rate. This method has good application prospects in improving the energy utilization efficiency and reliability of distribution networks.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Lingyu Liang, Wenqi Huang, Shang Cao, Huanming Zhang, Xiangyu Zhao, and Hanju Li "Smart energy-load matching strategy for distribution networks based on deep learning and K-means clustering algorithm", Proc. SPIE 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 1278864 (25 September 2023); https://doi.org/10.1117/12.3004608
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
 6 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
Data storage
Deep learning
Power supplies
Power grids
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top