An Evaluation of NILM Approaches on Industrial Energy-Consumption Data

Kalinke, Florian; Bielski, Paweł Michał; Singh, Snigdha; Fouché, Edouard; Böhm, Klemens

doi:10.1145/3447555.3464863

Cited by 27 publications

(5 citation statements)

References 9 publications

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“…Compared with household and commercial loads, industrial load signals have the following different characteristics [5]:…”

Section: Training Stagementioning

confidence: 99%

Achieving Sustained Improvement in Identification Accuracy with a Semi-Supervised Learning Approach for NILM Systems

Zhang¹,

Li²,

Guo³

et al. 2023

Preprint

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<p>Non-Intrusive Load Monitoring (NILM) systems are widely used for energy management, but their accuracy can be limited by the removal of monitoring equipment after the initial training phase. This removal results in a lack of ongoing training data and impairs the ability to improve recognition accuracy. To address this challenge, this research proposes a novel semi-supervised learning approach for NILM systems. The approach is based on a previously proposed temporal convolutional network-conditional random field (TCN-CRF) model. It uses optimal interval filtering and unanimous voting to control the quality of pseudo-labels. The approach identifies high-precision segments in the sequence-to-sequence algorithm as semi-supervised learning targets and generates pseudo-labels, allowing for continuous self-supervised learning and sustained improvement in recognition accuracy. The results show that the proposed method can effectively combine unlabeled data to improve model accuracy, and achieve up to 14% highest improvement in algorithm accuracy over fully self-supervised methods in small sample training.</p>

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“…Compared with household and commercial loads, industrial load signals have the following different characteristics [5]:…”

Section: Training Stagementioning

confidence: 99%

Achieving Sustained Improvement in Identification Accuracy with a Semi-Supervised Learning Approach for NILM Systems

Zhang¹,

Li²,

Guo³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Load disaggregation is a complex task that can be solved by different approaches as an optimization problem [11,12]. Extensive use of load disaggregation techniques has been reported to support better energy management, demonstrating the potential of this technique to provide energy demand forecast and an overall reduction in energy consumption [13]. The detailed analysis of the isolated power signals of individual machines can additionally provide more information on the specific activity being carried out by those machines [5].…”

Section: Introductionmentioning

confidence: 99%

Detailed Energy Analysis of a Sheet-Metal-Forming Press from Electrical Measurements

Carrillo,

Díaz Dorado,

Cidrás Pidre

et al. 2023

Energies

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This paper presents a methodology that allows for the detection of the state of a sheet-metal-forming press, the parts being produced, their cadence, and the energy demand for each unit produced. For this purpose, only electrical measurements are used. The proposed analysis is conducted at the level of the press subsystems: main motor, transfer module, cushion, and auxiliary systems, and is intended to count, classify, and monitor the production of pressed parts. The power data are collected every 20 ms and show cyclic behavior, which is the basis for the presented methodology. A neural network (NN) based on heuristic rules is developed to estimate the press states. Then, the production period is determined from the power data using a least squares method to obtain normalized harmonic coefficients. These are the basis for a second NN dedicated to identifying the parts in production. The global error in estimating the parts being produced is under 1%. The resulting information could be handy in determining relevant information regarding the press behavior, such as energy per part, which is necessary in order to evaluate the energy performance of the press under different production conditions.

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“…Specifically, we study how to measure EV charging events economically and efficiently by careful analysis of the aggregated residential load curve-already widely measured using smart meters-rather than by using a dedicated power meter. This is a special case of the wellknown non-intrusive load monitoring (NILM) problem [11,28,37]: instead of identifying all appliances and their energy consumption from the aggregated load curve, we only study EV charging.…”

Section: Introductionmentioning

confidence: 99%

EVSense

Wang

Tang

Wang

et al. 2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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As the number of electric vehicles (EVs) increases, large-scale residential EV charging will burden the power grid, posing problems for both planning and operations. Promptly capturing EV charging events can help mitigate this problem. However, most existing grid operators lack dedicated sensors for residential EV monitoring. This motivates non-intrusive load monitoring (NILM) as a technique to gain fine-grain EV charging information. We present EVSense, a robust deep neural network (DNN) based model for non-intrusive EV charging detection. We also show how to use federated transfer learning (FTL) to deploy our system on resource-constrained edge devices. This makes EVSense a feasible solution for large-scale EV monitoring. We evaluate EVSense on both real-world and synthetic datasets and find that it can achieve higher precision and more robust charging detection compared to existing learning-based and rule-based approaches. We also find that, due to the use of FTL, EVSense also shows excellent scalability despite diversity in residential load profiles, sampling rates, and seasons. CCS CONCEPTS• Computing methodologies → Neural networks; • Computer systems organization → Embedded and cyber-physical systems.

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An Evaluation of NILM Approaches on Industrial Energy-Consumption Data

Cited by 27 publications

References 9 publications

Achieving Sustained Improvement in Identification Accuracy with a Semi-Supervised Learning Approach for NILM Systems

Achieving Sustained Improvement in Identification Accuracy with a Semi-Supervised Learning Approach for NILM Systems

Detailed Energy Analysis of a Sheet-Metal-Forming Press from Electrical Measurements

EVSense

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