Explainable NILM Networks

Murray, David M.; Stankovic, Vladimir; Stanković, Vladimir

doi:10.1145/3427771.3427855

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…Explainable AI (XAI) attempts to promote a more transparent and trustworthy AI through the creation of methods that make the function and predictions of machine learning systems comprehensible to humans, without sacrificing performance levels [ 50 ]. Explainable NILM networks proposed by [ 51 ] try to understand the inner workings of the machine learning models used for NILM.…”

Section: A Brief Nilm Literature Reviewmentioning

confidence: 99%

“…Understanding the outputs of the networks contributes to improving the NILM model structure, highlights the relevant features and aspects of the data used for making the decision, provides a clearer picture of the accuracy of the models (since a single accuracy number is often insufficient), as well as inherently provides a level of trust in the value of the provided consumption feedback to the NILM end-user. Murray et al [ 51 , 110 ] investigated how eXplainable AI (XAI-based) approaches can be used to explain the inner workings for NILM deep learning models and examined why the network performs or does not perform well in certain cases. Explainable AI is utilized to analyze input data and address biases, especially when the NILM algorithms are tested in unseen houses, in order to improve the performance of the models [ 110 ].…”

Section: Trustworthiness In Nilm Algorithms: Can We Trust Ai In Nilm ...mentioning

confidence: 99%

See 1 more Smart Citation

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Kaselimi

Protopapadakis

Voulodimos

et al. 2022

Sensors

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Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach the desired performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. Currently, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios. Thus, the complexity of the algorithms, transferability, reliability, practicality, and, in general, trustworthiness are the main issues of interest. This review narrows the gap between the early immature NILM era and the mature one. In particular, the paper provides a comprehensive literature review of the NILM methods for residential appliances only. The paper analyzes, summarizes, and presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and introduces the research dilemmas that should be taken into consideration by researchers to apply NILM methods. Finally, we show the need for transferring the traditional disaggregation models into a practical and trustworthy framework.

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Section: A Brief Nilm Literature Reviewmentioning

confidence: 99%

Section: Trustworthiness In Nilm Algorithms: Can We Trust Ai In Nilm ...mentioning

confidence: 99%

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Kaselimi

Protopapadakis

Voulodimos

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…x max and x min correspond to maximal and minimal values. These values can be maximal or minimal values of the training dataset, parameters fixed by the authors [53], or quantile values [40]. In order to make the statistics of the data less sensitive to outliers, [44] transformed them with an arcsinh before normalizing.…”

Section: Preprocessingmentioning

confidence: 99%

Review on Deep Neural Networks Applied to Low-Frequency NILM

et al. 2021

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This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview on the state of the research up to November 2020, and secondly, to identify worthwhile open research topics. Accordingly, we first review the many degrees of freedom of these approaches, what has already been done in the literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported mean absolute error (MAE) and F1-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10 s, a large field of view, the usage of generative adversarial network (GAN) losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning, and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

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“…x max and x min correspond to maximal and minimal values. These values can be maximal or minimal values of the training dataset, parameters fixed by the authors [68], or quantile values [69]. In order to make the statistics of the data less sensitive to outliers, [70] transformed them with an arcsinh before normalizing.…”

Section: Preprocessingmentioning

confidence: 99%

Review on Deep Neural Networks Applied to Low-Frequency NILM

Huber¹,

Calatroni²,

Rumsch³

et al. 2021

Preprint

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This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e. data with sampling rates lower than the AC base frequency. We first review the many degrees of freedom of these approaches, what has already been done in literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported MAE and F$_1$-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10\,s, a large field of view, the usage of GAN losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

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Explainable NILM Networks

Cited by 12 publications

References 17 publications

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Review on Deep Neural Networks Applied to Low-Frequency NILM

Review on Deep Neural Networks Applied to Low-Frequency NILM

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