Synthetic-to-Real Dataset Transfer Learning for Non-Intrusive Load Monitoring
Author:
Affiliation:
1. College of Electrical and Information Engineering, Hunan University,Changsha,P.R. China
2. Power Supply Service Management, Center of State Grid Jiangxi Electric Power Co., Ltd.,Nanchang,P.R. China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10296973/10297127/10297255.pdf?arnumber=10297255
Reference20 articles.
1. AMBAL: Realistic load signature generation for load disaggregation performance evaluation
2. SmartSim: A device-accurate smart home simulator for energy analytics
3. A synthetic energy dataset for non-intrusive load monitoring in households
4. Synthetic dataset generation for non-intrusive load monitoring in commercial buildings
5. Towards reproducible state-of-the-art energy disaggregation
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. Smart Homes, Smart Choices: Using Big Data to Boost Energy Efficiency and Environmental Sustainability;Electric Power Components and Systems;2024-04-17
2. Synthetic-to-Real Domain Adaptation for Nonintrusive Load Monitoring via Reconstruction-Based Transfer Learning;IEEE Transactions on Instrumentation and Measurement;2024
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