A Comprehensive Review on the NILM Algorithms for Energy Disaggregation

Verma, Akriti; Anwar, Adnan; Mahmud, M. A. Parvez; Ahmed, Mohiuddin; Kouzani, Abbas

doi:10.48550/arxiv.2102.12578

Cited by 3 publications

(4 citation statements)

References 18 publications

(27 reference statements)

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“…Load monitoring is the concept that refers to the process of identifying and acquiring the consumption measurement of the load in an electrical system [14]. It is essential for effective management and accurate control of the electrical energy consumed [15,16], as it provides detailed information to electricity end-users about their consumption patterns and power consumption habits. In households, load monitoring is assessed by directly monitoring each device or by breaking down the total power signal [14,17].…”

Section: Load Monitoringmentioning

confidence: 99%

Comparative Study on Load Monitoring Approaches

Tokam,

Ouro-Djobo

2023

Applied Sciences

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Without an appropriate monitoring system, the condition/state of electrical appliances/devices in operation in households cannot be fully assessed, resulting in uncontrolled expenses. The purpose of load monitoring techniques is to save electricity consumption. With proper controls, overconsumption of energy can be reduced and unwanted activity that can lead to unnecessary electricity consumption can be eliminated. To achieve this, two approaches are used. The first approach, which says that each device is monitored by means of individual meters or metering devices, is called intrusive load monitoring (ILM) and requires expensive deployment of metering devices for its use. In contrast to the first one, the second approach is non-intrusive load monitoring (NILM), which monitors electricity consumption without the need for any intrusion. In this configuration, the total energy consumed is disaggregated into the individual consumption of each load. With progress/advances in artificial intelligence, this approach is gaining interest with influences in other areas of research. Knowing that these developed techniques aim to encourage the occupants of dwellings to save energy by optimizing their electricity consumption, the paper presents a comparative study of these approaches, in order to highlight the strengths as well as the weaknesses of each of them. It is therefore a means of offering researchers the opportunity to make choices according to the orientations given to the research work.

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Section: Load Monitoringmentioning

confidence: 99%

Comparative Study on Load Monitoring Approaches

Tokam,

Ouro-Djobo

2023

Applied Sciences

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“…Basically, it is a matter of monitoring electricity consumption by appropriate technological means. In energy management, load monitoring refers to the process of identifying and acquiring measurements of electricity consumption of the loads of an electrical system [2][3][4]. Inspired by technological advances and particularly by machine learning methods, load monitoring is attracting a lot of interest [5].…”

Section: Load Monitoring Context and Approachesmentioning

confidence: 99%

“…The nonintrusive approach is more advantageous than the intrusive one, as it requires only one sensor to be installed, very low maintenance, in addition to being affordable, and non-intrusive to the user [9]. Moreover, it represents an interesting alternative to the intrusive approach, as stated by Verma et al [4] in their work, in addition to encouraging many researchers to adapt it to various research domains to obtain new results. For G. W. Hart [10], who pioneered the concept of non-intrusive load monitoring, identifying the contribution of each appliance to the total electricity consumption of a house was the beginning of energy savings.…”

Section: Non-intrusive Load Monitoringmentioning

confidence: 99%

Non-Intrusive Load Monitoring (NILM), Interests and Applications

TOKAM,

OURO-DJOBO

2023

J. Nig. Soc. Phys. Sci.

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In developing effective energy management mechanisms, new concepts have been developed to provide new approaches. Non-intrusive load monitoring (NILM) is an approach that was originally developed to allow the occupants of a room to identify the contribution of each appliance to the total electricity consumption of the room through a single point measurement device. The aim is to provide customers with information that will enable them to act as `` ` consum'actors", i.e., people who undertake to change their electricity consumption habits for an objective cause. The progress of artificial intelligence in its various forms (machine learning, big data, internet of things) have greatly contributed to increase the interest of NILM among researchers in different fields. Indeed, some of them are adapting this concept to research areas such as water, transport, health, the environment and agriculture. In this context, applications in these fields have been developed to show the potential and benefits of using this approach. In addition to presenting non-intrusive load monitoring (NILM) in its general framework, this article presents the interests and applications of this approach in various fields.

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“…To this end, the use of non-intrusive load monitoring (NILM) or energy disaggregation has been proposed [20] as an alternative that can reduce the amount of infrastructure required to provide meaningful insights on the appliance level, by disaggregating the signal of a household's total consumption to the consumption of the individual appliances. This field has recently seen significant progress with the use of various algorithms [21], toolkits [22], and/or public datasets with pre-collected data that can be used for training said algorithms [23,24].…”

Section: Introductionmentioning

confidence: 99%

Equipment- and Time-Constrained Data Acquisition Protocol for Non-Intrusive Appliance Load Monitoring

Koasidis,

Marinakis,

Doukas

et al. 2023

Energies

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Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appliance level without needing a smart meter plugged in to each load. However, the use of public datasets with pre-collected data employed for energy disaggregation is associated with limitations regarding its compatibility with random households, while gathering data on the ground still requires extensive, and hitherto under-deployed, equipment and time commitments. Going beyond these two approaches, here, we propose a novel data acquisition protocol based on multiplexing appliances’ signals to create an artificial database for energy disaggregation implementations tailored to each household and dedicated to performing under conditions of time and equipment constraints, requiring that only one smart meter be used and for less than a day. In a case study of a Greek household, we train and compare four common algorithms based on the data gathered through this protocol and perform two tests: an out-of-sample test in the artificially multiplexed signal, and an external test to predict the household’s appliances’ operation based on the time series of a real total consumption signal. We find accurate monitoring of the operation and the power consumption level of high-power appliances, while in low-power appliances the operation is still found to be followed accurately but is also associated with some incorrect triggers. These insights attest to the efficacy of the protocol and its ability to produce meaningful tips for changing energy behaviours even under constraints, while in said conditions, we also find that long short-term memory neural networks consistently outperform all other algorithms, with decision trees closely following.

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A Comprehensive Review on the NILM Algorithms for Energy Disaggregation

Cited by 3 publications

References 18 publications

Comparative Study on Load Monitoring Approaches

Comparative Study on Load Monitoring Approaches

Non-Intrusive Load Monitoring (NILM), Interests and Applications

Equipment- and Time-Constrained Data Acquisition Protocol for Non-Intrusive Appliance Load Monitoring

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