State identification of home appliance with transient features in residential buildings

Yan, Lei; Xu, Runnan; Sheikholeslami, Mehrdad; Li, Yang; Li, Zuyi

doi:10.1007/s11708-022-0822-z

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…This approach has demonstrated promising results in appliance-level energy disaggregation [16] and has become a foundational technique in NILM research. Similarly, [17] focuses on exploiting the sparsity property of hidden Markov models (HMMs) to perform online real-time non-intrusive load monitoring (NILM). By leveraging the inherent sparsity structure in NILM problems, the proposed method achieves efficient and accurate energy disaggregation.…”

Section: Literature Surveymentioning

confidence: 99%

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Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

Nutakki,

Mandava

2024

EURASIP J. Adv. Signal Process.

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The integration of smart homes into smart grids presents numerous challenges, particularly in managing energy consumption efficiently. Non-intrusive load management (NILM) has emerged as a viable solution for optimizing energy usage. However, as smart grids incorporate more distributed energy resources, the complexity of demand-side management and energy optimization escalates. Various techniques have been proposed to address these challenges, but the evolving grid necessitates intelligent optimization strategies. This article explores the potential of data-driven NILM (DNILM) by leveraging multiple machine learning algorithms and neural network architectures for appliance state monitoring and predicting future energy consumption. It underscores the significance of intelligent optimization techniques in enhancing prediction accuracy. The article compares several data-driven mechanisms, including decision trees, sequence-to-point models, denoising autoencoders, recurrent neural networks, long short-term memory, and gated recurrent unit models. Furthermore, the article categorizes different forms of NILM and discusses the impact of calibration and load division. A detailed comparative analysis is conducted using evaluation metrics such as root-mean-square error, mean absolute error, and accuracy for each method. The proposed DNILM approach is implemented using Python 3.10.5 on the REDD dataset, demonstrating its effectiveness in addressing the complexities of energy optimization in smart grid environments.

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Section: Literature Surveymentioning

confidence: 99%

“…In the case of without calibration and without load division, the error after load assignment pt1 and the estimated power consumption is given in (17). In the case of with calibration and without load division, the error term pt2 is changed as shown (18), and consequently, error minimization equation is modified.…”

Section: Algorithmmentioning

confidence: 99%

Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

Nutakki,

Mandava

2024

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

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State identification of home appliance with transient features in residential buildings

Cited by 1 publication

References 23 publications

Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

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