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

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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.

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Quek, Y. T., Woo, W. L., & Logenthiran, T. (2020). Load Disaggregation Using One-Directional Convolutional Stacked Long Short-Term Memory Recurrent Neural Network. IEEE Systems Journal, 14(1), 1395–1404. https://doi.org/10.1109/JSYST.2019.2919668

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