Research on deep-learning-based photovoltaic output estimation and multi-layer collaborative load prediction in distribution networks

Mingfeng Xu; Zeming Lv; Hefei Wang; Shan Lang; Shaowei Wang; Shihao Li

doi:10.1117/12.3025696

28 February 2024 Research on deep-learning-based photovoltaic output estimation and multi-layer collaborative load prediction in distribution networks

Mingfeng Xu, Zeming Lv, Hefei Wang, Shan Lang, Shaowei Wang, Shihao Li

Author Affiliations +

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 1307143 (2024) https://doi.org/10.1117/12.3025696
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

In recent years, the penetration rate of distributed photovoltaics in distribution networks has been continuously increasing. The coupling of photovoltaic output with actual load with randomness forms a generalized load with greater uncertainty, posing a serious challenge to the safe and stable operation of the distribution network. Accurate distributed photovoltaic output and load forecasting is the foundation for ensuring the safety and economy of distribution network operation. However, currently most distributed photovoltaics are installed after the electricity meter and lack proprietary metering devices, making photovoltaic output invisible to distribution network operators, greatly affecting photovoltaic output prediction. In addition, the load of the distribution network has a multi-layered structure from bottom to top, and existing load forecasting methods are difficult to meet the aggregation and consistency requirements of multi-layered loads, which increases the operational decision-making burden of the distribution network. In this context, this study applies deep learning techniques such as generative adversarial networks and long short-term memory neural networks to achieve accurate estimation of photovoltaic output and collaborative prediction of multi-layer loads in distribution networks containing distributed photovoltaics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingfeng Xu, Zeming Lv, Hefei Wang, Shan Lang, Shaowei Wang, and Shihao Li "Research on deep-learning-based photovoltaic output estimation and multi-layer collaborative load prediction in distribution networks", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 1307143 (28 February 2024); https://doi.org/10.1117/12.3025696

ACCESS THE FULL ARTICLE

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
14 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Photovoltaics

Data modeling

Education and training

Deep learning

Machine learning

Neurons

Statistical modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years