Transfer Learning for Non-Intrusive Load Monitoring

D'Incecco, Michele; Squartini, Stefano; Zhong, Mingjun

doi:10.48550/arxiv.1902.08835

Cited by 3 publications

(8 citation statements)

References 34 publications

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“…The model proposed in [19] achieves an excellent balance between complexity and performance, outperforming both FHMM and previous DL models. This algorithm has been tested on several public datasets; in particular, its generalization capability was tested in [23] by providing a system with data on homes belonging to datasets other than the one presented in training. Thus, in this work, DL models are used to demonstrate the feasibility of a solution based on a small, low-power microcontroller for real-time energy consumption monitoring.…”

Section: I2mtc Paper Id: 1570780806mentioning

confidence: 99%

“…Table I in DL called overfitting [29], where the model becomes too tightly fitted to the training data, hindering its ability to perform well on new data. For each appliance, validation is performed using the entire dataset of a single house, according to the methodology established in [23], during model presentation. The data for training are preprocessed before being used for training.…”

Section: B Training Settingsmentioning

confidence: 99%

“…Table III shows the percentage relative errors and MAEs obtained for the different appliances over the entire dataset for each house (half-year basis). For a thorough explanation of the results in Table III, Table IV provides additional metrics obtained from a related study [23]. The authors of [23] trained a sequence-to-point DL model on the REFIT dataset [22], as was done in this work, but they assessed its generalization capability using the UK-DALE [28] and REDD [27] houses as test sets.…”

Section: 𝑆𝐴𝐸 =mentioning

confidence: 99%

“…For a thorough explanation of the results in Table III, Table IV provides additional metrics obtained from a related study [23]. The authors of [23] trained a sequence-to-point DL model on the REFIT dataset [22], as was done in this work, but they assessed its generalization capability using the UK-DALE [28] and REDD [27] houses as test sets. These datasets, along with REFIT, are widely used to evaluate the performance of energy disaggregation algorithms..…”

Section: 𝑆𝐴𝐸 =mentioning

confidence: 99%

See 3 more Smart Citations

An Embedded Deep Learning NILM System: A Year-Long Field Study in Real Houses

Mari,

Bucci,

Ciancetta

et al. 2023

IEEE Trans. Instrum. Meas.

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Abstract-Nonintrusive load monitoring (NILM) systems are used to identify the energy consumption patterns of individual devices in an electrical system, but broadening their market availability is a significant challenge. In this paper, a NILM system using edge processing is proposed, in which energy consumption data are processed directly on the device installed at the monitored facility. Specifically, it uses a sequence-to-point approach based on a convolutional neural network implemented on an Arm Cortex-M7 microcontroller. This paper also reports the results of an extensive 12-month testing phase. The NILM system was installed in two real houses in central Italy to evaluate its installation and potential application in real-world scenarios. This study presents a promising solution that enables the widespread adoption of NILM systems by reducing their implementation cost and complexity and addresses the privacy concerns associated with cloud-based data processing. The results of our real-world testing provide compelling evidence of the potential of the proposed NILM system in various applications, including smart homes, building automation, and industrial energy management.

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Section: I2mtc Paper Id: 1570780806mentioning

confidence: 99%

Section: B Training Settingsmentioning

confidence: 99%

Section: 𝑆𝐴𝐸 =mentioning

confidence: 99%

Section: 𝑆𝐴𝐸 =mentioning

confidence: 99%

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An Embedded Deep Learning NILM System: A Year-Long Field Study in Real Houses

Mari,

Bucci,

Ciancetta

et al. 2023

IEEE Trans. Instrum. Meas.

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“…Two transfer learning schemes for NILM, appliance transfer learning (ATL) and cross-domain transfer learning (CTL), are proposed in [25]. The authors investigate in transferability of Deep Neural Networks for NILM and find that sequence-topoint learning is transferable in the sense that this technique can be applied to test data without fine-tuning, given that the training and test data are in a similar domain.…”

Section: Related Workmentioning

confidence: 99%

On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring

Klemenjak¹,

Faustine²,

Makonin³

et al. 2019

Preprint

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To assess the performance of load disaggregation algorithms it is common practise to train a candidate algorithm on data from one or multiple households and subsequently apply cross-validation by evaluating the classification and energy estimation performance on unseen portions of the dataset derived from the same households. With an emerging discussion of transferability in Non-Intrusive Load Monitoring (NILM), there is a need for domain-specific metrics to assess the performance of NILM algorithms on new test scenarios being unseen buildings. In this paper, we discuss several metrics to assess the generalisation ability of NILM algorithms. These metrics target different aspects of performance evaluation in NILM and are meant to complement the traditional performance evaluation approach. We demonstrate how our metrics can be utilised to evaluate NILM algorithms by means of two case studies. We conduct our studies on several energy consumption datasets and take into consideration five state-of-the-art as well as four baseline NILM solutions. Finally, we formulate research challenges for future work.

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Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect

Chung

Zhang

2023

Applied Sciences

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Advances in machine learning and artificial intelligence (AI) techniques bring new opportunities to numerous intractable tasks for operation and control in modern electric distribution systems. Nevertheless, AI applications for such grids as cyber-physical systems encounter multifaceted challenges, e.g., high requirements for the quality and quantity of training data, data efficiency, physical inconsistency, interpretability, and privacy concerns. This paper provides a systematic overview of the state-of-the-art AI methodologies in the post-pandemic era, represented by transfer learning, deep attention mechanism, graph learning, and their combination with reinforcement learning and physics-guided neural networks. Dedicated research efforts on harnessing such recent advances, including power flow, state estimation, voltage control, topology identification, and line parameter calibration, are categorized and investigated in detail. Revolving around the characteristics of distribution system operation and integration of distributed energy resources, this paper also illuminates prospects and challenges typified by the privacy, explainability, and interpretability of such AI applications in smart grids. Finally, this paper attempts to shed light on the deeper and broader prospects in the realm of smart distribution grids by interoperating them with smart building and transportation electrification

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Transfer Learning for Non-Intrusive Load Monitoring

Cited by 3 publications

References 34 publications

An Embedded Deep Learning NILM System: A Year-Long Field Study in Real Houses

An Embedded Deep Learning NILM System: A Year-Long Field Study in Real Houses

On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring

Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect

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