Load identification based on optimized fuzzy C-means state extraction

Lü, Peng; Wang, Fanrong; Liu, Yang; Xiang, Kun

doi:10.1109/ecie52353.2021.00073

Cited by 3 publications

(1 citation statement)

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“…This is mainly due to their easy implementation compared with DL models. In fact, several NILM frameworks have been using K‐means, 144 C ‐means, 145 FCM, 146 density‐based spatial clustering of applications with noise (DBSCAN), 147 one‐class SVM, 148 local outlier factor (LOF), 149 competitive agglomeration (CA), 150 and so forth. Moreover, dimensionality reduction is still part of various NILM frameworks, such as principal component analysis (PCA), 145 linear discriminant analysis (LDA), 4 fuzzy‐neighbors preserving analysis based QR‐decomposition (FNPA‐QR), 83 kernel PCA (KPCA), 151 t ‐distributed stochastic neighbor embedding ( t ‐SNE), 152 singular value decomposition (SVD), 153 and truncated SVD (TSVD) 154 …”

Section: Overview Of Recent Smart Nilm Algorithmsmentioning

confidence: 99%

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

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Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first Int J Intell Syst. 2022;37:7124-7179. wileyonlinelibrary.com/journal/int investigated, including data collection devices and public data sets. Next, event-based and non-eventbased NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

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