Non-Intrusive Load Monitoring

Fortuna, Luigi; Buscarino, Arturo

doi:10.3390/s22176675

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…In ILM [8,9], each electric load is monitored by a separate sensor and the information acquired from all sensors can be centrally processed by cloud-end. While in NILM [6,7,10], only one monitor is required for each family or cell. It captures electric signals (such as voltage, current, and so on) at the commercial power input and transimits them to cloud server in which workload information of all loads are interpreted with algorithms.…”

Section: Related Workmentioning

confidence: 99%

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Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

He,

Liu,

Zhang

et al. 2024

Preprint

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The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

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Section: Related Workmentioning

confidence: 99%

“…An efficient electric power management system is dependent on its electric load monitoring module [5][6][7], which can be realized by intrusive or non-intrusive approaches. Compared with Intrusive Load Monitoring (ILM), Non-Intrusive Load Monitoring (NILM) has more advantages (see Section II for details).…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

He,

Liu,

Zhang

et al. 2024

Preprint

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“…In FHMMS, a model consists of multiple independent HMMs and the output is essentially a combination of all the hidden states. During the last decade, deep learning solutions have come to dominate the energy disaggregation research field, producing state-of-the-art solutions [ 22 ]. Kelly and Knottenbelt [ 23 ] were the first to apply deep learning in order to tackle the problem of NILM, introducing three novel architectures.…”

Section: Introductionmentioning

confidence: 99%

Variational Regression for Multi-Target Energy Disaggregation

Gkalinikis

Nalmpantis

Vrakas

2023

Sensors

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Non-intrusive load monitoring systems that are based on deep learning methods produce high-accuracy end use detection; however, they are mainly designed with the one vs. one strategy. This strategy dictates that one model is trained to disaggregate only one appliance, which is sub-optimal in production. Due to the high number of parameters and the different models, training and inference can be very costly. A promising solution to this problem is the design of an NILM system in which all the target appliances can be recognized by only one model. This paper suggests a novel multi-appliance power disaggregation model. The proposed architecture is a multi-target regression neural network consisting of two main parts. The first part is a variational encoder with convolutional layers, and the second part has multiple regression heads which share the encoder’s parameters. Considering the total consumption of an installation, the multi-regressor outputs the individual consumption of all the target appliances simultaneously. The experimental setup includes a comparative analysis against other multi- and single-target state-of-the-art models.

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“…Alternatively, Non-intrusive Load Monitoring (NILM) [6] offers a more practical and cost-effective means to estimate the energy consumption of individual devices. NILM has emerged as a crucial approach in this domain, leveraging advanced computational capabilities to estimate individual electrical device consumption using a single smart meter sensor [7]. In the literature, there are various categories of methods used in NILM.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Development and Application of an Open Power Meter Suitable for NILM

Rodríguez-Navarro,

Portillo,

Martínez

et al. 2023

Inventions

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In the context of the global energy sector’s increasing reliance on fossil fuels and escalating environmental concerns, there is an urgent need for advancements in energy monitoring and optimization. Addressing this challenge, the present study introduces the Open Multi Power Meter, a novel open hardware solution designed for efficient and precise electrical measurements. This device is engineered around a single microcontroller architecture, featuring a comprehensive suite of measurement modules interconnected via an RS485 bus, which ensures high accuracy and scalability. A significant aspect of this development is the integration with the Non-Intrusive Load Monitoring Toolkit, which utilizes advanced algorithms for energy disaggregation, including Combinatorial Optimization and the Finite Hidden Markov Model. Comparative analyses were performed using public datasets alongside commercial and open hardware monitors to validate the design and capabilities of this device. These studies demonstrate the device’s notable effectiveness, characterized by its simplicity, flexibility, and adaptability in various energy monitoring scenarios. The introduction of this cost-effective and scalable tool marks a contribution to the field of energy research, enhancing energy efficiency practices. This research provides a practical solution for energy management and opens advancements in the field, highlighting its potential impact on academic research and real-world applications.

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Non-Intrusive Load Monitoring

Abstract: Non-Intrusive load monitoring (NILM) represents an emerging strategy based on the application of sevaral multidisciplinary topics [...]

Cited by 4 publications

References 15 publications

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

Variational Regression for Multi-Target Energy Disaggregation

Development and Application of an Open Power Meter Suitable for NILM

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