Load Disaggregation Using One-Directional Convolutional Stacked Long Short-Term Memory Recurrent Neural Network

Yang Thee Quek, Wai Lok Woo, Thillainathan Logenthiran

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
25 Downloads (Pure)

Abstract

Reliable information about the active loads in the energy system allows for effective and optimized energy management. An important aspect of intelligent energy monitoring system is load disaggregation. The proliferation of direct current (dc) loads has spurred the increasing research interest in extra low voltage (ELV) dc grids. Artificial intelligence, such as deep learning algorithms of stacked recurrent neural network (RNN), improved results on a variety of regression and classification tasks. This paper proposes a 1-D convolutional stacked long short-term memory RNN technique for the bottom-up approach in load disaggregation using single sensor multiple loads ELV dc picogrids. This eliminates the requirement for communication and intelligence on every load in the grid. The proposed technique was applied on two different dc picogrids to test the algorithm's robustness. The proposed technique produced excellent result of over 98% accuracy for smart loads and over 99% accuracy for dumb loads in ELV dc picogrid.
Original languageEnglish
Pages (from-to)1395-1404
Number of pages10
JournalIEEE Systems Journal
Volume14
Issue number1
Early online date26 Jun 2019
DOIs
Publication statusPublished - 2 Mar 2020
Externally publishedYes

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