Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for...

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Bibliographic Details
Main Authors: Zengxin Pu, Yu Huang, Min Weng, Yang Meng, Yunbin Zhao, Gengsheng He
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-04-01
Series:Frontiers in Energy Research
Subjects:
deep learning
non-intrusive load monitoring
denoising autoencoder
weather feature
calendar feature
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2024.1361916/full

Internet

https://www.frontiersin.org/articles/10.3389/fenrg.2024.1361916/full

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