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Title: A demand-side load event detection algorithm based on wide-deep neural networks and randomized sparse backpropagation
Authors: Li, Chen
Liang, Gaoqi
Zhao, Huan
Chen, Guo
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Li, C., Liang, G., Zhao, H. & Chen, G. (2021). A demand-side load event detection algorithm based on wide-deep neural networks and randomized sparse backpropagation. Frontiers in Energy Research, 9, 720831-.
Journal: Frontiers in Energy Research
Abstract: Event detection is an important application in demand-side management. Precise event detection algorithms can improve the accuracy of non-intrusive load monitoring (NILM) and energy disaggregation models. Existing event detection algorithms can be divided into four categories: rule-based, statistics-based, conventional machine learning, and deep learning. The rule-based approach entails hand-crafted feature engineering and carefully calibrated thresholds; the accuracies of statistics-based and conventional machine learning methods are inferior to the deep learning algorithms due to their limited ability to extract complex features. Deep learning models require a long training time and are hard to interpret. This paper proposes a novel algorithm for load event detection in smart homes based on wide and deep learning that combines the convolutional neural network (CNN) and the soft-max regression (SMR). The deep model extracts the power time series patterns and the wide model utilizes the percentile information of the power time series. A randomized sparse backpropagation (RSB) algorithm for weight filters is proposed to improve the robustness of the standard wide-deep model. Compared to the standard wide-deep, pure CNN, and SMR models, the hybrid wide-deep model powered by RSB demonstrates its superiority in terms of accuracy, convergence speed, and robustness.
ISSN: 2296-598X
DOI: 10.3389/fenrg.2021.720831
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 Li, Liang, Zhao and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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