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dc.contributor.authorLi, Chenen_US
dc.contributor.authorLiang, Gaoqien_US
dc.contributor.authorZhao, Huanen_US
dc.contributor.authorChen, Guoen_US
dc.identifier.citationLi, 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-.
dc.description.abstractEvent 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.en_US
dc.relation.ispartofFrontiers in Energy Researchen_US
dc.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.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleA demand-side load event detection algorithm based on wide-deep neural networks and randomized sparse backpropagationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsIndex Terms-Event Detectionen_US
dc.subject.keywordsEvent Classificationen_US
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