J. Nig. Soc. Phys. Sci. 5 (2023) 1208

Journal of the
Nigerian Society

of Physical
Sciences

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

Leonce W. Tokama, Sanoussi S. Ouro-djoboa,b

aCentre d’Excellence Régional pour la Maı̂trise de l’Électricité (CERME), University of Lomé, Lomé, Togo
bLaboratory on Solar Energy, Department of Physics, Faculty of Science, University of Lomé, Lomé, Togo

Abstract

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.

DOI:10.46481/jnsps.2023.1208

Keywords: Non-intrusive load monitoring (NILM), power consumption, interests, applications

Article History :
Received: 25 March 2023
Received in revised form: 06 May 2023
Accepted for publication: 10 May 2023
Published: 27 May 2023

c© 2023 The Author(s). Published by the Nigerian Society of Physical Sciences under the terms of the Creative Commons Attribution 4.0 International license

(https://creativecommons.org/licenses/by/4.0). Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Communicated by: O. J. Oluwadare

1. Introduction

The upheavals caused by climate change, the decline in fos-
sil fuel resources and the increase in global energy demand are
some of the reasons why we need to review the way we manage
and consume electrical energy. To respond to these worrying
challenges, solutions have been proposed, including demand-
side management (DSM). According to the French Agency for
the Environment and Energy Management (ADEME), DSM

∗Corresponding author tel. no: +237 693 42 80 13
Email address: wtokam@univ-lome.tg (Leonce W. Tokam )

refers to the grouping of energy saving actions implemented
for the end consumer and not for the energy producer. In many
countries around the world, few energy saving actions are pro-
moted. This creates an imbalance between supply and demand
that needs to be addressed. Faced with this concern, which
has major economic implications, technological solutions have
been proposed to help reduce this imbalance and thus reduce
CO2 emissions. Previous studies have shown that when energy
supply companies communicate information on their consump-
tion patterns to end-users of electricity, either directly or indi-
rectly, the impact is that energy savings of over 12% are ob-
served [1]. However, capitalizing on technological advances

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Tokam & Ouro-djobo / J. Nig. Soc. Phys. Sci. 5 (2023) 1208 2

such as artificial intelligence and machine learning is helping
to extend this type of action. For many scientists, the context is
the development and improvement of existing solutions that can
address these concerns. One of the solutions being exploited is
load monitoring and specifically non-intrusive load monitoring
(NILM), which is being used to reduce electricity consumption
in households and buildings of all kinds. From the beginning,
the NILM concept was limited to the field of electricity. The re-
sults obtained as well as the improvements regularly proposed,
have greatly contributed to the renewed interest that is currently
observed. The applications developed have aroused the interest
of researchers in other fields to adapt this concept to their differ-
ent fields of research. For the latter, it is a question of presenting
the great advantage that NILM offers, through its applications
in fields such as water, the environment, transport (maritime and
rail), health (teleconsultation and assistance to elderly people
living alone) and agriculture. In the rest of the paper, we briefly
present the context and the different approaches to load moni-
toring, the general framework for the implementation of NILM,
the interests and applications of NILM in the fields where it has
been exploited, and finally a conclusion.

2. Load monitoring, context and approaches

Real-time feedback on the electricity consumption of a
house or other building to end consumers reflects the desire to
influence consumption patterns, with the intention of improv-
ing them. The aim is to reduce energy demand by limiting
over-consumption and waste. To achieve this, it is essential to
have the load signatures (measurable electrical data) that can
be detected by the installed measuring device(s). 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
[2–4]. Inspired by technological advances and particularly by
machine learning methods, load monitoring is attracting a lot
of interest [5]. Depending on the approach used and the results
sought, it can be intrusive or non-intrusive.

2.1. Intrusive load monitoring

Intrusive load monitoring (ILM) is needed when it is neces-
sary to go inside a house or other premise to install a measuring
device for each appliance present. The measuring device can
be a wireless sensor, a digital meter, or any other object that
fulfils this role. This approach is laborious as it requires the in-
stallation. Depending on the level of intrusion, three types of
intrusive load monitoring can be distinguished [6,3]:

Type 1: metering device are installed at the circuit breaker
and only measure the consumption of one part of the house.

Type 2: the metering devices are placed at the outlets, so as
to monitor one or more appliances at the same time.

Type 3: A measuring device is installed on each appliance.
Despite the accurate measurement results, this approach is

not very cost-effective for the reasons mentioned above.

2.2. Non-intrusive load monitoring

In contrast to the intrusive approach, non-intrusive load
monitoring (NILM) refers to the process by which the over-
all electricity consumption of a building is identified without
intrusion from a single point of measurement, and then the de-
tailed consumption of each device is provided [2,7,8]. The non-
intrusive approach is more advantageous than the intrusive one,
as it requires only one sensor to be installed, very low mainte-
nance, 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 var-
ious research domains to obtain new results. For G. W. Hart
[10], who pioneered the concept of non-intrusive load monitor-
ing, identifying the contribution of each appliance to the total
electricity consumption of a house was the beginning of energy
savings. The operation of the NILM integrates four (4) steps re-
spectively: data acquisition, event detection, feature extraction
and load identification. Based on machine learning (ML) and
artificial intelligence (AI) techniques, the NILM system iden-
tifies variations in voltage and current signals, which can be
compared to fingerprints or signatures of each device. This
identification is only possible thanks to dedicated algorithms
belonging to the supervised, semi-supervised and unsupervised
learning algorithm families [11].

3. General framework and concept of non-intrusive load
monitoring

In NILM, each step performs a specific task, so that the de-
sired objective (energy disaggregation) is easily achieved. The
success of the process of disaggregating the total energy con-
sumed into individual load consumptions provides information
on the level of accuracy and performance of the chosen feature
extraction and learning methods. In the following, we present
the contribution of each step in the NILM process.

3.1. Data acquisition

The energy consumed in an electrical installation is counted
by an electrical meter and therefore represents a data that can be
exploited in several ways. In the NILM concept, data acquisi-
tion or recording is essential, in that the sampling frequency de-
termines the types of information [12]. For example, at low fre-
quencies, the low sampling rate considerably limits the range of
devices that can be detected, and the choice of scheme (steady
state, transient or non-conventional) also has an influence. In
some NILM systems, transient characteristics of loads or noise
generated by devices may be required at high sampling rates
to obtain accurate measurements [13]. The choice of electrical
characteristics to be recorded depends on the frequency class ;
high frequency when the sampling frequency is between 10 and
200 kHz, medium frequency when it is between 50 Hz and 10
kHz, and low frequency for frequencies between 1 and 50 Hz
[14]. As a reminder, smart meters belong to the low frequency
range and are suitable for low sampling rate collections [15].

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3.2. Event detection

This step corresponds to the detection of switches
(ON/OFF, steady state to transient state and vice versa) that
occurs during the operation of the devices. These detected
switches provide information on the variations in energy con-
sumption during operation (source). In the literature, two ap-
proaches to non-intrusive load monitoring have been developed
depending on the algorithm to be used. These are event-based
non-intrusive load monitoring, which requires an event detec-
tion step during its implementation as discussed by some au-
thors in their work [16,17], and non-event-based load monitor-
ing [18]. In the case of the first approach, the performance of
the algorithms to be used is crucial. During the feature extrac-
tion and classification (learning) phase, they allow to limit the
number of false commutations likely to appear and affect the
performance of the algorithm. In contrast to the previous ap-
proach, the non-event-based NILM does not include an event
detection step. Here, Hidden Markov Models (HMM), as well
as their variants that we present in section 3.4.3, are usually
used as a learning algorithm. This algorithm has the advantage
of processing each global signal sample (power, voltage, cur-
rent, etc.), and in the end identifying each device [19–20].

It should be added that, for devices that obey ON/OFF,
multi-state and continuous operation, respectively, it is easier
to detect them. On the other hand, continuously variable device
(CVD) devices are more complicated to detect, as their energy
consumption is arbitrarily modified. In the literature, different
types of devices commonly encountered in energy disaggrega-
tion are presented [1,13,17]. These include devices for:

Type I: These are appliances with only two operating states
(ON/OFF) and consuming constant energy throughout their
operation, such as an incandescent light bulb, electric kettle,
toaster or microwave. These appliances are called resistive ap-
pliances because the reactive energy they give off is almost zero.

Type II: This category includes finite state machines (FSM)
or multi-state machines, because they have a finite number of
distinct states that can be repeated. In addition, the state transi-
tions that take place during operation are identified using varia-
tions in energy consumption over a period of time. Appliances
such as electric cookers, refrigerators with automatic defrost-
ing, washing machines, variable speed fans are considered to
belong to this family.

Type III: loads belonging to this group are called continu-
ously variable Devices (CVD). They are characterized by elec-
tronic devices that vary their power consumption over time,
without a fixed number of states. For these devices, it is dif-
ficult to easily allocate their energy consumption. Examples in-
clude dimmers, electric drills and split air conditioners. Figure
1 shows the operating states of the first three types of appli-
ances.

Type IV: Appliances that can be operated at a constant rate
throughout the day, such as TV receivers, smoke detectors and
telephone sets, belong to this group.

The continuous nature of the operating state of devices be-
longing to the last group cannot be represented in Figure 1, but
it can be done in a pictorial way.

In view of this non-exhaustive inventory, it should be re-
called that the main challenge of NILM remains the disaggre-
gation of the energy consumed by each of the loads belonging
to these categories, both separately and simultaneously, at reg-
ular or different time intervals.

Figure 1. Illustration of the operating status of the devices in the various groups

3.3. Features extraction
Once the detection of the event is done, the essential fea-

tures of the event are extracted for the identification of the de-
vice that caused it [21]. Feature extraction plays a crucial role
in learning device signatures [1]. Furthermore, these features
can be classified into three categories: steady state, transient
and non-conventional.

3.3.1. Steady state
A steady state is said to exist when the characteristics of

the load remain unchanged for a certain period of time. These
are characteristics such as : real (P), reactive (Q), apparent
(S) power, root mean square (RMS), power factor (PF), fre-
quency (F) and time domain characteristics, voltage-current
paths, voltage noise, which can be used for load identification
[1,3]. In the steady state, the load signature represents the in-
formation found in the analysis between two consecutive op-
erational steady states [22]. In steady state, the extraction of
the characteristics is done at low frequency, as this favors the
detection of small variations that occur continuously, due to
the nature of the loads. Moreover, this is much easier than a
momentary indication. It should be added that in the transient
scheme, the steady state signatures are the set satisfying the
zero-loop sum constraint, where the sum of power changes in
any cycle of state transitions is zero. However, recall that some
device signatures may overlap due to their similar characteris-
tics, thus making load identification and disaggregation com-
plex. To solve this difficulty, V-I trajectories are used, in order
to distinguish device events [1]. Figure 2 shows the different
schemes, depending on the features to be extracted and trained
in the NILM algorithms.

3.3.2. Transient state
When a device is switched on, an event (transient state) oc-

curs for a period of time before the stable state indicating the
operation of the device. It can be observed in both (ON/OFF)
and multi-state devices [23]. A transient is characterized by a

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Figure 2. Categorization of NILM systems based on features extraction schemes

relaxation time, a damping rate or a quality factor. It usually
occurs when there is a change in the system. In the steady
state, when two or more loads are in similar power ranges,
event detection becomes less and less accurate [1]. The use of
transients can overcome this shortcoming, by accurately iden-
tifying the events that take place. To make this possible, so-
phisticated, high-cost, high-accuracy equipment adapted to the
circumstances is recommended [24]. The features (transient
power, inrush current, transient voltage noise, transient V-I
paths and higher order harmonics) extracted for examination
are highly sampled (from kHz to MHz) [1,3,25,26].

3.3.3. Non-conventional features
The non-conventional regime includes characteristics that

do not belong to the steady state or the transient regime. This
group includes a combination of conventional, contextual, be-
havioural or indicator features of electrical devices [24]. These
include features such as start time, end time, peak time, time
of day, frequency of appliance use, current signal eigenvalues,
light detection and temperature, which are used to add addi-
tional information to the conventional features [1,3,23].

3.4. Load identification and inference

This is the next step after features extraction. During the
load identification phase, the signatures extracted from the
overall power signal of the loads are analyzed, with a view to
labelling the loads [15,23]. Also, to learn the detected signa-
tures, learning algorithms are used, while the deductions of the
load states are performed using inference algorithms, following
the observed aggregate power data [15]. The algorithms used

in this stage of the NILM belong to either the supervised, semi-
supervised or unsupervised families of learning algorithms. In-
deed, supervised learning algorithms require large amounts of
data (datasets or dataset) on the characteristics of the individ-
ual loads extracted, in order to train the algorithm to classify
the loads in operation [3,27]. In addition, this data needs to be
labeled, so that the algorithm correlates the input and output
data perfectly during the training phase. For example, when the
algorithm is trained to deduce whether the signal shape is that
of a coffee maker, with supervised learning, it creates a label
for each signal used in the training data, indicating whether the
signal is that of a coffee maker in operation or not.

Semi-supervised learning algorithms use a set of labeled
and unlabeled data. In order to learn successfully, they need
to train small amounts of labeled data [3,23]. These algorithms
fall between supervised ones using labeled data and unsuper-
vised ones not using labelled data. It has been shown that the
use of unlabeled data, in combination with labeled data, signif-
icantly improves the quality of learning. Furthermore, labelling
data sometimes requires the intervention of a human user. How-
ever, when datasets become very large, this operation can be
tedious. In this case, semi-supervised learning, which requires
only a few labels, is of obvious practical interest. As an ex-
ample, co-learning is an example of semi-supervised learning,
where two classifiers learn a set of data, but each using a differ-
ent, ideally independent, set of features. If the data are objects
to be classified according to their nature, one might use size and
the other shape for example.

With unsupervised learning, the algorithms are trained on
unlabeled data. They scan the data sets for significant connec-
tions. In addition, in the context of non-intrusive load mon-
itoring, pre-processing of unlabeled input data is not useful,

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thus favoring NILM applications operating in real time [15].
Furthermore, Klemenjack et al. [27] add that unsupervised
algorithms must both perform load disaggregation and detec-
tion of connected devices in the circuit they monitor. However,
there are discrepancies in the assessment of this form of learn-
ing. According to Tezde et al. [23], although unsupervised
learning is suitable for NILM, it is more difficult to achieve
satisfactory results than supervised learning. Moreover, it is
up to the consumer to define (label) which device and state
should belong to the clusters formed during learning. However,
in the research that is being done, NILM is making remark-
able progress, thanks to the construction of much more reliable
and inexpensive unsupervised learning models, as reported in
[5,28].

3.4.1. Supervised learning methods
In their implementation, supervised learning methods make

use of labeled training data, and the detected signals are used
to establish load signatures [1]. However, these methods can be
classified into two approaches [3,23]:

Optimization approaches: learning problems are evalu-
ated as optimization problems. That is, using the learning expe-
rience, they help to optimize the performance criteria by com-
paring the features extracted from loads to the stored features
of loads in a database, in order to obtain the closest possible
match. In their work, authors such as [29,30] have used genetic
algorithms (GA) as learning methods. In the literature, Zoha et
al. [5] point out that apart from pattern recognition approaches,
optimization approaches are also suitable learning methods for
load disaggregation.

Pattern recognition-based approaches: In this approach,
patterns are recognized by machine learning algorithms. Pat-
tern recognition is defined as the classification of the data ex-
tracted from patterns and/or their previously acquired represen-
tations. In sum, solving pattern recognition problems is solving
classification problems. In the literature, there are many algo-
rithms that have been used to achieve this. These include Sup-
port Vector Machine (SVM) algorithms [16,31–36], k-Nearest
Neighbours (k-NN) for load classification based on power sig-
nals [37–41], Decision Trees [42–44], Bayes classifiers [45–
46 ], Artificial Neural Networks [47–49], and Hidden Markov
Methods [50,51] - which have been shown to be capable of
introducing temporal and state change information. However,
Hidden Markov Methods (HMM) can be used as a supervised
or unsupervised approach, depending on the results sought.

3.4.2. Semi-supervised learning methods
These are methods that combine small amounts of labeled

data with large amounts of unlabeled data during training
[52,53]. It is a special case of weak supervision. In the lit-
erature, few authors have used these methods, although they
present interesting performances, as demonstrated by Barsim et
al. [52] in their work, where they use self-training as a learning
tool to solve the energy disaggregation problem. They point
out that semi-supervised learning tools have the ability to re-
duce the labelling effort required, by providing a learning dis-
aggregation system whose performance gradually increases as

it observes more unlabeled aggregate measures. According to
Li et al. [53], semi-supervised learning algorithms are a good
alternative to supervised learning algorithms that require the la-
belling of all the loads connected in the power system, in addi-
tion to the fluctuations of the main power supply that must be
trained. Thus, using graph-based multi-label classification, they
manage to obtain better results than the state of the art on some
datasets such as REDD (Reference Energy Disaggregation Data
Set), BLUED or AMPds.

3.4.3. Unsupervised learning methods
As mentioned earlier, unsupervised learning methods do not

need labeled data to run their algorithms. This makes them suit-
able for NILM applications running in real time. Furthermore,
the current trend is that due to their low operating cost and re-
liability, they are being further developed and improved [15].
These authors add that in the literature, unsupervised learning
methods can be classified into three subgroups, namely: unsu-
pervised approaches that use unlabeled data in training to build
a database of devices; unsupervised approaches that use labeled
data from known buildings to build load models that are then
used to disaggregate the energy consumed in unknown build-
ings; and finally unsupervised approaches that do not require
training prior to energy disaggregation.

There are authors in the literature who have used unsu-
pervised learning methods for various reasons. For example,
K-means algorithms have been used by Yang et al. [54] to
analyze the clustering formed by matching after detection of
events (ON/OFF) of appliances and by Buddhahai et al. [55]
to perform partitioning of load data by clusters in number of
power states, which helped to identify the power state of appli-
ances such as water heater, air conditioner with accuracy and
F-score values above 89%. Abubakar et al. [13] and Gopinath
et al. [1] mention in their research that expectation maximiza-
tion has been used as an unsupervised learning algorithm to
detect unknown load states. To identify ON/OFF states of de-
vices Gopinath et al.[1] adopt the concept of hidden events
and for real and active power consumption the concept of ob-
served events, which together with the transition matrix and
initial states will give good accuracies. In order to improve
the results previously obtained with Hidden Markov Models
(HMM), different variants have been explored, such as Facto-
rial HMM (FHMM), Conditional FHMM (CFHMM), Factorial
Hidden Semi Markov Model (FHSMM), Conditional Factorial
Hidden Semi Markov Model (CFHSMM) [1,23]. From the ob-
servation made, it appears that the CFHSMM performs better
than the HMM variants for energy disaggregation and that the
CFHMM is the second best in its performance. An explanation
is given for these HMM variants, in order to better understand
them:

1. FHMM: this is the extension of HMM where several hid-
den state variables are used instead of one hidden state
variable.

2. CFHMM: This is a variant used to represent the state se-
quence. However, it requires the addition of features such
as time and sensor measurements.

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3. FHSMM: This is an extension of FHMM that uses an al-
ternative probability density function for the occupation
time of the devices.

4. CFHSMM: is the combination of FHSMM and CFHMM.

At the end of the load identification and learning process,
the overall aggregate consumption of the loads is decomposed
into individual consumptions of the loads installed in the power
system. Thus, using detailed information obtained from each
load, the consumer is informed of what each load represents in
the total consumption.

3.5. Non-intrusive load monitoring dataset

Datasets are databases consisting of naturally or syntheti-
cally obtained energy consumption measurements (as they re-
duce costs, increase time savings by eliminating corrupted data
and missing values [23]). They are the result of long measure-
ment campaigns in households in various European and North
American countries. In addition, these accessible datasets are
also used to train the different algorithms of the learning meth-
ods mentioned above [27].

To be tested and improved, in order to prove their per-
formance when called upon, the energy disaggregation algo-
rithms need training data from the available energy consump-
tion datasets. The datasets are intended to provide researchers
with real-world energy consumption data, so that during the
training phase, real-world energy consumption data achieves
very good match rates and high levels of accuracy. The Refer-
ence Energy Disaggregation Data Set (REDD) is the first pub-
licly available energy dataset released by the Massachusetts In-
stitute of Technology (MIT) in 2011 [56]. This dataset aggre-
gates consumption data from six (6) households in the United
States obtained at low and high frequencies over relatively short
time periods.

Following the development of REDD, a number of datasets
have emerged, including the Plug-Level Appliance Identifica-
tion Dataset (PLAID) that Gao et al. [57] presented in in their
paper, which is a 30 kHz sampled and labeled dataset contain-
ing 1876 individually measured device from 17 different appli-
ance types of 330 different makes and models, with 1314 simul-
taneous operating records from 13 appliance types (i.e. mea-
surements obtained when several appliances were active simul-
taneously). United Kingdom recording Domestic Appliance-
Level Electricity (UK-DALE) presented by Kelly et al. [58]
is an open-access dataset for households with data recorded at
high frequency (16 kHz) describing the overall and actual con-
sumption situations of individual appliances. Other examples
include BLUED, GREED, AMPds and AMPds2.

4. Interests and applications of NILM

NILM is a concept that was first developed by George
William Hart of the Massachusetts Institute of Technology
(MIT) in the early 1980s to monitor and evaluate the number
and type of loads and their individual energy consumption. De-
spite the revolution that this technology heralded at the time of

its inception, it was not a great success, due to its complex-
ity (very large statistical data to be processed, limited comput-
ing power of computers). However, the rise of artificial intelli-
gence, as well as improvements in computer performance, have
greatly contributed to the renewed interest in NILM among re-
searchers. Figure 3 shows the number of publications per year
for the research topic ”non-intrusive load monitoring” in the
ScienceDirect database. Also, the continuous discoveries and
improvements proposed over the years have led to adaptations
of the concept to other research areas

Figure 3. Number of publications on non-intrusive load monitoring from the
ScienceDirect database

In addition to the characteristics of non-intrusive load mon-
itoring (NILM) to provide information on the status of appli-
ances, the amount of electricity consumed per appliance or in-
formation on the electricity consumption habits of the occu-
pants, it also has potential in areas other than electricity. Its
non-intrusive nature gives it an originality that adapts to the
specificities of each field, where it can play an essential role
in the search for expected solutions.

In electricity, NILM has seen a lot of progress in terms of
improving the performance of learning algorithms. In practice,
there are mobile applications that allow detailed monitoring of
electricity consumption, so that electricity bills can be reduced.
For the same authors, NILM has enabled some researchers to
develop applications that detect illegally connected loads in
households and buildings, as well as the presence or absence
of people. The aim is to identify respectively the theft of elec-
trical energy that may occur and cause overconsumption [3].
In another case, the development of the Internet of Things and
smart homes has led to the creation of new applications aimed
at enabling consumers to save energy [12,59]. NILM toolkits
(NILMTK) have also been developed to monitor loads during
operation [60].

The daily use of water in a household, a building, or an
agglomeration varies according to factors such as age, income
level, lifestyle or geography. Thus, to considerably reduce over-
consumption and waste, communication actions aimed at in-
forming customers about their consumption patterns and habits
should be encouraged for common satisfaction. Real-time feed-
back is an action that many water supply companies need to
adopt in order to limit water wastage. Inspired by the concept
of non-intrusive load monitoring developed in electricity, Kim
et al. [61] have adapted it to water consumption monitoring
by proposing an easy-to-install calibration system. The system
uses wireless vibration sensors installed in the pipes to inform

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users about the amount of water used, when and where it is
used. In addition, its non-intrusive and self-contained nature re-
quires no plumbing expertise for installation. In a similar vein,
Schantz et al. [62] carried out similar work to the above. It was
found that the designed system is accurate and its use by house-
holds on a large scale would contribute to its popularization and
to the achievement of large water savings.

In the transport sector, the very high level of use of means
of transport means that the prevention of failures, which can
occur during multiple journeys, should be taken into account
and integrated into maintenance systems. To this end, some re-
searchers have deemed it relevant to integrate NILM into war-
ships, freighters or cruise ships, in order to present its useful-
ness and the great interest it brings. Thus, Nation et al. [63]
have in their work used NILM for the detection and isolation
of faults in automated systems on board ships, showing that
with NILM, as well as the event (ON/OFF) of the ship’s grey
water discharge pump. In the same vein, Lindahl et al. [64]
have looked at the detection of faults on board ships through
non-intrusive load monitoring. According to them, the use
of NILM through its applications contributes to improving the
health of ships by monitoring engine room equipment and pro-
viding early detection of faults that can cause irreversible dam-
age and inestimable losses.

There are also some applications of non-intrusive load mon-
itoring in rail transport. In this respect, Mariscotti [65] pre-
sented the interest of the efficiency of NILM in AC railways.
Indeed, considering only the identification and extraction steps
of the electrical characteristics (current, voltage and harmonics)
of the NILM, the performances obtained through classification
and clustering methods show that the NILM is not as relevant
as in households, due to the fact that the rolling stock obeys
regulations dictated by very strict standards.

In a completely different area, Batra et al. [66] used NILM
to develop a technique that predicts household size, occupancy,
income and age. In the health sector, a non-intrusive anomaly
detection monitoring system has been developed for the elderly
to report interference with daily activities [67]. The developed
device is based on the concepts of NILM, as mentioned by these
authors. The study was motivated by the authors’ desire to pro-
vide facilities to improve their health care and well-being by
making them more independent. Later, the same authors Alcalá
et al. [68], then proposed a paper in which they examined the
home care monitoring system through the data of smart meters
installed in these elderly people. Later, Dai et al. [69] devel-
oped in the context of telehealth and for elderly people with re-
duced mobility and dementia, a model for recognizing patterns
of activities of daily living (ADL) based on the NILM. For the
specific case of NILM applied to ADL, the demand response
is able to deliver real-time information to the involved parties.
However, there is still a threat to the privacy of users. Indeed,
the identification of user activities is based on user consump-
tion profiles. There are still some flaws in this system, notably
the exposure of private habits. On this aspect, Gong et al. [70]
presented a succinct plan to preserve user privacy for demand
in smart grids. The developed and proposed scheme allowed
consumers to protect their identity by participating in the de-

mand response program, but offered consumers the possibility
to publish their identity under certain scenarios, such as legal
disputes.

In agriculture, pest diseases are a huge obstacle to increas-
ing crop yields. They result in lower harvests, leading to huge
losses of income. The case of soybean cultivation is a clear ex-
ample, where in some research centers it is difficult to identify
the symptoms and types of pest diseases in soybean cultiva-
tion [71]. In order to provide suitable solutions, Simunjutak et
al. [71] have developed an application that helps researchers
to identify soybean pests by classification. Although the accu-
racy of the results depends on the amount of data trained, such
applications should be improved and disseminated.

The search for effective solutions to climate change remains
an ongoing challenge, and innovative solutions combining arti-
ficial intelligence and big data are now common. To this end,
Kee et al. [72] conducted a study on the impact of non-intrusive
load monitoring on CO2 emissions in Malaysia. The results
of the study revealed that by making use of energy efficiency
practices in daily electricity consumption, overall CO2 emis-
sions would be reduced by 10.2% in the country. Furthermore,
by incorporating energy efficiency practices into daily electric-
ity consumption, overall CO2 emissions would be reduced by
10.2% in the country. Furthermore, by integrating non-intrusive
load monitoring into these consumption practices, the rate of
CO2 emission reduction would increase from 45% to just over
60% by 2030. In conclusion, the authors state that the applica-
tion of energy efficiency practices based on NILM is a valuable
benchmark for the development and adoption of energy effi-
ciency policies to control CO2 emissions.

In sum, the applications presented in this work reflect the
interest that many fields of research and activities would have
in integrating artificial intelligence, machine learning and big
data for well-functioning solutions that incur reduced opera-
tional costs.

5. Conclusion

Given the energy and climate challenges, it is clear that
more effort needs to be made to use solutions that meet the re-
quirements of the established difficulties. Energy management
is one such solution. Developing ways to drastically reduce the
observed wastage would help to reduce the carbon footprint, the
adverse effects of which the world feels on a daily basis. To this
end, the concept of non-intrusive load monitoring (NILM) was
first developed to allow household occupants to identify how
much electricity each appliance consumed as part of the over-
all household consumption. Advances in artificial intelligence
have led to major revolutions that have generated growing in-
terest, so that the concept of non-intrusive load monitoring is no
longer confined to the electricity sector alone, but also to other
sectors such as transport, medicine, water management and en-
vironmental protection. The applications developed in this area
show the great interest in extending it and proposing new ways
of managing and controlling activities. In addition, progress is
continually being made to improve the performance of learn-
ing algorithms dedicated to NILM, as well as all other essential

7



Tokam & Ouro-djobo / J. Nig. Soc. Phys. Sci. 5 (2023) 1208 8

points contributing to its performance. Despite the fact that it is
considered as an alternative to be popularized on a large scale to
encourage energy savings, and that it also has many advantages
such as its affordable cost, its installation at a single measur-
ing point, and the absence of intrusion into the household for
its operation, it still remains for many researchers an immature
technology that needs to be perfected.

Acknowledgments

The authors express their profound gratitude to the World
Bank for funding this research through CERME (Centre
d’Excellence Régional pour la Maitrise de l’Electricité). Tanks
also the referees for the positive enlightening comments and
suggestions, which 10 have greatly helped us in making im-
provements to this paper

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