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A platform for research: civil engineering, architecture and urbanism
NILM applications: Literature review of learning approaches, recent developments and challenges
Graphical abstract Display Omitted
Highlights A critical analysis of the properties of the most widely used residential, commercial and industrial datasets for NILM. Comprehensive presentation of all the feature extraction and pre-processing techniques that have been applied in energy disaggregation domain. Up-to-date detailed overview of the existing NILM approaches, focusing mainly on the latest machine and deep learning methods. Key summaries are provided based on visualizations of NILM performance across various datasets/appliances/methods. Assessment of the existing performance evaluation methods and suggestion of a proper comparability and evaluation framework.
Abstract This paper presents a critical approach to the non-intrusive load monitoring (NILM) problem, by thoroughly reviewing the experimental framework of both legacy and state-of-the-art studies. Some of the most widely used NILM datasets are presented and their characteristics, such as sampling rate and measurements availability are presented and correlated with the performance of NILM algorithms. Feature engineering approaches are analyzed, comparing the hand-made with the automatic feature extraction process, in terms of complexity and efficiency. The eolution of the learhes through time is presented, making an effort to assess the contribution of the latest state-of-the-art deep learning models to the problem. Performance evaluation methods and evaluation metrics are demonstrated and it is attempted to define the necessary requirements for the conduction of fair evaluation across different methods and datasets. NILM limitations are highlighted and future research directions are suggested.
NILM applications: Literature review of learning approaches, recent developments and challenges
Graphical abstract Display Omitted
Highlights A critical analysis of the properties of the most widely used residential, commercial and industrial datasets for NILM. Comprehensive presentation of all the feature extraction and pre-processing techniques that have been applied in energy disaggregation domain. Up-to-date detailed overview of the existing NILM approaches, focusing mainly on the latest machine and deep learning methods. Key summaries are provided based on visualizations of NILM performance across various datasets/appliances/methods. Assessment of the existing performance evaluation methods and suggestion of a proper comparability and evaluation framework.
Abstract This paper presents a critical approach to the non-intrusive load monitoring (NILM) problem, by thoroughly reviewing the experimental framework of both legacy and state-of-the-art studies. Some of the most widely used NILM datasets are presented and their characteristics, such as sampling rate and measurements availability are presented and correlated with the performance of NILM algorithms. Feature engineering approaches are analyzed, comparing the hand-made with the automatic feature extraction process, in terms of complexity and efficiency. The eolution of the learhes through time is presented, making an effort to assess the contribution of the latest state-of-the-art deep learning models to the problem. Performance evaluation methods and evaluation metrics are demonstrated and it is attempted to define the necessary requirements for the conduction of fair evaluation across different methods and datasets. NILM limitations are highlighted and future research directions are suggested.
NILM applications: Literature review of learning approaches, recent developments and challenges
Angelis, Georgios-Fotios (author) / Timplalexis, Christos (author) / Krinidis, Stelios (author) / Ioannidis, Dimosthenis (author) / Tzovaras, Dimitrios (author)
Energy and Buildings ; 261
2022-02-10
Article (Journal)
Electronic Resource
English
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