Matching Theory Aided Federated Learning Method for Load Forecasting of Virtual Power Plant-Reference-Cited by-同舟云学术

Matching Theory Aided Federated Learning Method for Load Forecasting of Virtual Power Plant

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 17th International Conference on Mobility, Sensing and Networking (MSN)
language:
Short-container-title:

Author:

Yan Min¹,Wang Li¹,Wang Xuanyuan²,Li Liang¹,Xu Lianming³,Fei Aiguo¹

Affiliation:

1. Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China,100876

2. State Grid Jibei Electric Power Company Ltd.,Beijing,China,100054

3. Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China,100876

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9751460/9751461/09751534.pdf?arnumber=9751534

Reference22 articles.

1. Electrical Load Forecasting Using Edge Computing and Federated Learning

2. Short-term load forecasting in smart grid: A combined CNN and K-means clustering approach;dong;Proceeding of IEEE International Conference on Big Data and Smart Computing (BigComp),2017

3. Improving load forecasting based on deep learning and K-shape clustering

4. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

5. Matching-Theory-Based Low-Latency Scheme for Multitask Federated Learning in MEC Networks

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advancing Power System Services With Privacy-Preserving Federated Learning Techniques: A Review;IEEE Access;2024

2. Robust federated deep reinforcement learning for optimal control in multiple virtual power plants with electric vehicles;Applied Energy;2023-11