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

Quek, Yang Thee, Woo, Wai Lok and Logenthiran, Thillainathan (2019) Load Disaggregation Using One-Directional Convolutional Stacked Long Short-Term Memory Recurrent Neural Network. IEEE Systems Journal. ISSN 1932-8184 (In Press)

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Official URL: https://doi.org/10.1109/jsyst.2019.2919668

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.

Item Type: Article
Uncontrolled Keywords: Artificial intelligence, deep learning, direct current (dc) picogrid, energy management, energy monitoring, load disaggregation, long short-term memory (LSTM), neural network application
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 15 Oct 2019 10:08
Last Modified: 17 Oct 2019 10:45
URI: http://nrl.northumbria.ac.uk/id/eprint/41108

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