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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

Authors :
Zengxin Pu
Yu Huang
Min Weng
Yang Meng
Yunbin Zhao
Gengsheng He
Source :
Frontiers in Energy Research, Vol 12 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

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 demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the Denoising Autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.

Details

Language :
English
ISSN :
2296598X
Volume :
12
Database :
Directory of Open Access Journals
Journal :
Frontiers in Energy Research
Publication Type :
Academic Journal
Accession number :
edsdoj.0dd4d70a925e40ff82d189c125ef1543
Document Type :
article
Full Text :
https://doi.org/10.3389/fenrg.2024.1361916