CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12748 > Article
Paper
10 August 2023 Forecasting method for photovoltaic power based on data preprocessing and temporal convolutional network
Han Xiao, Daifei Liu
Author Affiliations +
Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 1274816 (2023) https://doi.org/10.1117/12.2689842
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China
Abstract
Power prediction of photovoltaic generation is very important to ensure the safety of the power system. It can assist the scheduling of smart grid and the operation and maintenance of photovoltaic stations. In order to improve the accuracy of photovoltaic power prediction, a temporal convolution network (TCN) model is constructed, which is combined with adaptive white noise complete integrated empirical mode decomposition (CEEMDAN) and wavelet threshold denoising. In the data preprocessing, the sequence of sample data is decomposed as modal components of different time and frequency scales with CEEMDAN method and the high-frequency modal components are denoised through wavelet threshold. By reconstructing the denoised components, the noise-reduced data set is obtained. In the process of TCN construction, the sample data used for modeling is from DKASC photovoltaic power generation public dataset. The experiment of modeling results show that the designed model can improve the accuracy of power prediction compared with the traditional prediction model. And the modeling method has feasibility and effectiveness.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Han Xiao and Daifei Liu "Forecasting method for photovoltaic power based on data preprocessing and temporal convolutional network", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 1274816 (10 August 2023); https://doi.org/10.1117/12.2689842
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
 8 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
Photovoltaics
Wavelets
Convolution
Denoising
Deep learning
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top