A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context

[Display omitted] •Review of the state-of-the-art data-driven methods for non-intrusive load monitoring.•Advanced NILM methods are accurate but more difficult to interpret and implement.•Existing NILM methods have not been evaluated in real-time testing scenarios.•Preparing appropriate training and...

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Vydané v:Energy and buildings Ročník 305; s. 113890
Hlavní autori: Rafiq, Hasan, Manandhar, Prajowal, Rodriguez-Ubinas, Edwin, Ahmed Qureshi, Omer, Palpanas, Themis
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 15.02.2024
Predmet:
Data-driven algorithms
Deep learning
Load disaggregation
NILM
Non-intrusive load monitoring
Real-time deployment
Deep learning
NILM
Real-time deployment
Non-intrusive load monitoring
Data-driven algorithms
Load disaggregation
ISSN:0378-7788
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Popis
Shrnutí:[Display omitted] •Review of the state-of-the-art data-driven methods for non-intrusive load monitoring.•Advanced NILM methods are accurate but more difficult to interpret and implement.•Existing NILM methods have not been evaluated in real-time testing scenarios.•Preparing appropriate training and test data is crucial for learning-based methods.•There is still more work needed to develop an accurate and useful NILM system. The rising demand for energy conservation in residential buildings has increased interest in load monitoring techniques by exploiting energy consumption data. In recent years, hundreds of research articles have been published that have mainly focused on data-driven non-intrusive load monitoring (NILM) approaches. Due to the high volume of research articles published in this domain, it has become necessary to provide a review of the up-to-date research in NILM and highlight the current challenges associated with its application. This paper reviews the state-of-the-art of NILM by following a structured assessment process to consider relevant and most recent documents in the literature. It presents the pros and cons of data-driven NILM methods, available datasets, and performance evaluation mechanisms. Even though research in NILM solutions has matured in recent years thanks to the use of deep learning models, there are still gaps in their effective deployment related to data requirements, real-time performance, and interpretability. Therefore, the paper also addresses the NILM development and implementation challenges and includes promising improvement measures that can be utilized to solve them.
ISSN:0378-7788
DOI:10.1016/j.enbuild.2024.113890