Time-Lapse Image Method for Classifying Appliances in Nonintrusive Load Monitoring

Seon, Joonho; Sun, Young Ghyu; Kim, Soohyun; Kim, Jin‐Young

doi:10.3390/en14227630

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…ML-based NILM methods have been proposed based on feature extraction, such as SVMs [57] and decision trees [58]. Recently, deep learning-based methods utilizing CNN and LSTM algorithms have been proposed to improve performance by capturing nonlinear features [59]. Furthermore, the Transformer-based NILM method has been proposed to improve prediction accuracy by capturing long-term dependencies in sequential data [60].…”

Section: Ml-based Emismentioning

confidence: 99%

Recent Trends and Issues of Energy Management Systems Using Machine Learning

Lee,

Seon,

Hwang

et al. 2024

Energies

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Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS.

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Section: Ml-based Emismentioning

confidence: 99%

Recent Trends and Issues of Energy Management Systems Using Machine Learning

Lee,

Seon,

Hwang

et al. 2024

Energies

Self Cite

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“…Either the CNN is trained to classify different RPs [129][130][131][132], or to predict time series values [115]. Such combinations of RPs and RQA measures with machine learning were successfully applied for transition detection, monitoring, and anomaly detection [80,[133][134][135][136].…”

Section: Recurrence and Machine Learningmentioning

confidence: 99%

Trends in recurrence analysis of dynamical systems

Marwan

Kraemer

2023

Eur. Phys. J. Spec. Top.

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The last decade has witnessed a number of important and exciting developments that had been achieved for improving recurrence plot-based data analysis and to widen its application potential. We will give a brief overview about important and innovative developments, such as computational improvements, alternative recurrence definitions (event-like, multiscale, heterogeneous, and spatio-temporal recurrences) and ideas for parameter selection, theoretical considerations of recurrence quantification measures, new recurrence quantifiers (e.g. for transition detection and causality detection), and correction schemes. New perspectives have recently been opened by combining recurrence plots with machine learning. We finally show open questions and perspectives for futures directions of methodical research.

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