Non-Intrusive Load Monitoring for Energy Consumption Disaggregation

Aravind, P.; Sarath, T. V.

doi:10.1109/icosec54921.2022.9951891

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%

“…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%

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Deep Neural Networks in Power Systems: A Review

Khodayar

Regan

2023

Energies

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Identifying statistical trends for a wide range of practical power system applications, including sustainable energy forecasting, demand response, energy decomposition, and state estimation, is regarded as a significant task given the rapid expansion of power system measurements in terms of scale and complexity. In the last decade, deep learning has arisen as a new kind of artificial intelligence technique that expresses power grid datasets via an extensive hypothesis space, resulting in an outstanding performance in comparison with the majority of recent algorithms. This paper investigates the theoretical benefits of deep data representation in the study of power networks. We examine deep learning techniques described and deployed in a variety of supervised, unsupervised, and reinforcement learning scenarios. We explore different scenarios in which discriminative deep frameworks, such as Stacked Autoencoder networks and Convolution Networks, and generative deep architectures, including Deep Belief Networks and Variational Autoencoders, solve problems. This study’s empirical and theoretical evaluation of deep learning encourages long-term studies on improving this modern category of methods to accomplish substantial advancements in the future of electrical systems.

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Section: Relu Neural Networksupporting

confidence: 68%