2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) 2022
DOI: 10.1109/icosec54921.2022.9951891
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Non-Intrusive Load Monitoring for Energy Consumption Disaggregation

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Cited by 4 publications
(2 citation statements)
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“…Energy disaggregation is another application in which the deep ReLU network shows high performance. This technique results in an F-score of 68.72 with a precision equal to 80.54 on the Reference Energy Disaggregation Dataset [16]. Similar high classification accuracies are reported for fault identification [91].…”
Section: Relu Neural Networksupporting
confidence: 68%
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“…Energy disaggregation is another application in which the deep ReLU network shows high performance. This technique results in an F-score of 68.72 with a precision equal to 80.54 on the Reference Energy Disaggregation Dataset [16]. Similar high classification accuracies are reported for fault identification [91].…”
Section: Relu Neural Networksupporting
confidence: 68%
“…Current artificial intelligence (AI) investigations on wind prediction [5][6][7], photovoltaic (PV) energy forecasting [8][9][10], state estimation [11,12], electrical grid generation [13,14], and energy segmentation [15,16] demonstrate that creating models fueled by data with less reliance on explicit ways of preprocessing (e.g., PCA) results in significantly improved prediction and regression quality. In this context, shallow artificial neural networks (ANNs) with few computational layers are proposed for load forecasting [17], probabilistic wind and solar power generation prediction [18], economic dispatch [19], voltage stability monitoring [20], system identification [21], nonlinear power system excitation control [22], and inductive coupling between overhead power lines and nearby metallic pipelines [23].…”
Section: Introductionmentioning
confidence: 99%