Search Results for author: Guoming Tang

Found 3 papers, 0 papers with code

FedNILM: Applying Federated Learning to NILM Applications at the Edge

no code implementations7 Jun 2021 Yu Zhang, Guoming Tang, Qianyi Huang, Yi Wang, Xudong Wang, Jiadong Lou

Non-intrusive load monitoring (NILM) helps disaggregate the household's main electricity consumption to energy usages of individual appliances, thus greatly cutting down the cost in fine-grained household load monitoring.

Federated Learning Model Compression +3

More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring

no code implementations1 Jun 2021 Yu Zhang, Guoming Tang, Qianyi Huang, Yi Wang, Hong Xu

Non-intrusive load monitoring (NILM) is a well-known single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usage of individual appliances.

blind source separation Non-Intrusive Load Monitoring

Plug and Play! A Simple, Universal Model for Energy Disaggregation

no code implementations7 Apr 2014 Guoming Tang, Kui Wu, Jing-sheng Lei, Jiuyang Tang

Energy disaggregation is to discover the energy consumption of individual appliances from their aggregated energy values.

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