# Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review

Mohammad Irani Azad  Faculty of ECE
Qom University of Technology
Qom, Iran
<EMAIL_ADDRESS>Roozbeh Rajabi  Faculty of ECE
Qom University of Technology
Qom, Iran
<EMAIL_ADDRESS>Abouzar Estebsari  School of the Built Environment and
Architecture
London South Bank University
London, United Kingdom
<EMAIL_ADDRESS>
###### Abstract

Demand-side management now encompasses more residential loads. To efficiently
apply demand response strategies, it’s essential to periodically observe the
contribution of various domestic appliances to total energy consumption. Non-
intrusive load monitoring (NILM), also known as load disaggregation, is a
method for decomposing the total energy consumption profile into individual
appliance load profiles within the household. It has multiple applications in
demand-side management, energy consumption monitoring, and analysis. Various
methods, including machine learning and deep learning, have been used to
implement and improve NILM algorithms. This paper reviews some recent NILM
methods based on deep learning and introduces the most accurate methods for
residential loads. It summarizes public databases for NILM evaluation and
compares methods using standard performance metrics.

###### Index Terms:

Smart Grids, NILM, Deep Learning, Energy Management.

##  I Introduction

The non-intrusive load monitoring (NILM) method has gained popularity in
recent years as a way to monitor appliance and electrical utility energy usage
in buildings and events (on/off) using a single energy meter. If consumers had
data on appliance-level energy usage, they could better understand their
energy consumption behavior and take action to reduce it. The aim of this
study is to present an overview of the latest algorithms currently being
investigated by researchers to create a precise non-intrusive load monitoring
(NILM) method for effective energy management. The article discusses the
potential applications of NILM across different fields, along with future
research objectives. The development of sustainable and smart cities has been
made possible by advancements in artificial intelligence (AI), smart meters,
the internet of things (IoT), and smart grids, as cited in [1] and [2].
Effective energy management is a crucial component of sustainable city
development, which aims to utilize resources responsibly, protect the
environment, and enhance society’s well-being. The objective of energy
management is to promote energy system self-reliance and sustainability [1].

Energy management involves monitoring and controlling electrical utilities to
optimize energy use and reduce consumption. However, with the increase in
energy needs, energy conservation has become a challenge in recent years [3].
Greater energy use can lead to an energy crisis, climate change, and a
negative impact on the economy [4]. It is estimated that the rise in carbon
emissions will increase global temperatures by 2.5 to 10 ∘C this century,
causing more frequent floods, droughts, a rise in sea level, and the spread of
infectious illnesses [5]. Therefore, it is essential to reduce carbon
emissions across all sectors, including construction, industry, and
transportation, to mitigate climate change. Researchers are working on
developing technology solutions for energy conservation [3]. Buildings are one
of the major contributors to energy consumption [6], with energy consumption
in this sector steadily increasing over time. In order to mitigate carbon
emissions, optimizing energy consumption in residential and commercial
buildings is crucial. This can be achieved through the construction or design
of energy-efficient structures, as well as improving energy usage in existing
buildings.

The paper is organized as follows. Section II introduces the mathematical
definition of the NILM problem. Section III discusses deep learning-based NILM
methods. Section IV provides a summary of the public NILM datasets. Section V
presents a comparison study of NILM methods, and finally, Section VI concludes
the paper.

##  II NILM Problem Definition

###  II-A Mathematical Problem Definition

The issue at hand can be described as follows: at a given time $t$, the total
active power consumed by a system is represented by $y(t)$, while $y_{i}(t)$
represents the active power consumed by the $i$th appliance at the same time.
The overall load is the sum of the energy consumed by individual appliances
and an unmeasured residual load, expressed as:

$y(t)=\sum_{i=1}^{N}y_{i}(t)+e(t),$ (1)

where $N$ denotes the number of appliances considered, and $e(t)$ represents
the undetermined residual load. The aim is to estimate $F(y(t))$ by
determining the values of $y_{i}(t)$, given only the value of $y(t)$, as:

$y_{1}(t),y_{2}(t),...,y_{i}(t),...,y_{N}(t)=F(y(t)),$ (2)

where $F$ is an operator that produces $N$ distinct values when applied to the
total active power. These numbers represent the most accurate estimate of the
power consumed by each appliance. It should be noted that $y_{i}(t)$ typically
does not reflect the entire set of home appliances but rather a subset of
them. As a result, the unknown term $e(t)$ takes into account the loads caused
by unmonitored appliances. If simultaneous measurements of the aggregate
consumption and load of each appliance are available, approximating the $F$
operator can be considered a supervised learning problem. When mainly
concerned with activation times and cumulative consumption, as is the case in
real situations, the estimated individual appliance consumption
($\hat{y}_{i}(t)$) can be obtained using functions that are constant over the
device’s activation period:

$\hat{y}_{i}(t)=p_{i}\hat{a}_{i}(t),$ (3)

where $p_{i}$ represents the average consumption of appliance $i$, and
$\hat{a}_{i}(t)$ represents an estimate of the activation state of the
particular appliance at time $t$. Its value is one if the device is in use and
uses energy, and zero otherwise. Therefore, starting with the aggregate load,
a technique is provided to derive the most accurate and feasible assessment of
the activation state of the appliances:

$\hat{a}_{1}(t),\hat{a}_{2}(t),...,\hat{a}_{i}(t),...,\hat{a}_{N}(t)=F_{a}(y(t)),$
(4)

After learning the average nominal consumption of the considered equipment,
one can use Equation 3 to estimate consumption.

###  II-B Appliance types

Based on their operational characteristics, appliances can be classified into
four types as discussed in [7]. Type I appliances have two modes of operation
- on and off. These include appliances such as kettles, toasters, and light
bulbs, which consume energy only when turned on. Type I appliances are
predominantly resistive with few linear reactive components. Type II
appliances are characterized as multi-state or finite state machines with a
limited number of operational states that may be run repeatedly. Changes in
these appliances’ states can be observed by monitoring the power consumption’s
falling/rising edges over time. Stove burners, refrigerators, and washing
machines are some examples of Type II appliances [7, 8]. Figure 1 demonstrates
the distinct appliance operation conditions.

Category III appliances, also known as Continuously Variable Devices (CVDs),
exhibit a non-repetitive power usage pattern, which poses a challenge for
energy consumption disaggregation. Examples of Type III appliances include
power drills and dimmer lights [8]. Type IV appliances are those that run
continuously for extended periods of time, typically lasting several days or
weeks. Examples of Type IV equipment include wireless telephone devices and
cable TV receivers [8]. Therefore, the Non-Intrusive Load Monitoring (NILM)
system is required to have the ability to differentiate between various types
of appliance events, which may happen concurrently or independently and at
varying time intervals.

Figure 1: A pictorial representation illustrates the different types of
appliances categorized based on their operating states.

##  III Deep Learning Based NILM methods

NILM techniques can be broadly categorized into two groups: supervised and
unsupervised methods [9]. In supervised NILM, individual appliance power usage
is used to train the models. On the other hand, unsupervised methods can only
utilize aggregate power usage data. Examples of unsupervised NILM techniques
include Hidden Markov models (HMM) [10, 11], factorial HMM (FHMM) [12, 13],
and techniques based on event detection and clustering [14, 15]. These
techniques have been thoroughly examined in previous studies [9, 16]. With the
advent of deep neural networks (DNNs), many neural network-based supervised
NILM techniques have been developed [17, 18]. Convolutional neural networks
(CNN) have also recently made significant advances [19, 20]. Graph signal
processing [21], HMM [12, 22, 13, 15, 23], and DNNs [24, 25] are commonly used
in suggested NILM approaches. As the cost of employing appliance data for
training has grown dramatically, researchers have focused on developing
unsupervised approaches and incorporating appliance models. Despite the
significant progress made in NILM research in recent years, challenges remain
in terms of application, identification accuracy, training time, and online
deployment techniques in smart metering frameworks.

###  III-A Event-Based Non-Intrusive Detection

The event-based NILM method is based on the concept of detecting and
categorizing events within a combined electrical signal. Figure 2 shows the
block diagram of the this approach. A robust event detector should be
developed to cope with noisy fluctuations and identify events with decay and
growth patterns, which is a bottleneck and inherent difficulty in existing
event detectors [26]. One approach includes the steps of event detection,
extraction, clustering, and matching in the event-based block [27]. It should
be noted that the accuracy of previous event-based frameworks is dependent on
the power features that are introduced. Since some appliances may have
identical active power curves but radically distinct reactive power trends,
increasing the number of features can enhance the accuracy of the appliance
model, particularly for non-linear loads. One of the advantages of
incorporating reactive power is that it enables discrimination between
different types of devices.

Figure 2: Block diagram scheme of event-based NILM.

###  III-B NILM Disaggregation by CNN

The proposed method [28] employs a convolutional neural network (CNN) that
takes the time interval of a home’s energy consumption as input and predicts
the activation status of each device at every time step. The network
architecture, referred to as Temporal Pooling NILM (TP-NILM), is an updated
version of Zhao et al.’s PSPNet (Pyramid Scene Parsing Network) used for
semantic image segmentation [29]. The TP-NILM follows the conventional
approach to image segmentation, with an encoder comprising pooling and
convolutional layers that enhance the feature space of the signal but decrease
its temporal resolution, and a decoder module that uses these features to
approximate the activation state of the devices at the original resolution. To
establish a temporal context that covers extended periods without compromising
the signal’s resolution, the TP-NILM incorporates a Temporal Pooling module
that accumulates features at various resolutions, enabling accurate
reconstruction of the activation state.

Figure 3: Outline of network structure for NILM by CNN.

Figure 3 illustrates the architecture of the network used in this study. The
encoder uses a rectified linear unit activation function, batch-independent
normalization downstream of the activations, and a regularization dropout
layer, with three convolutional filters interleaved by max-pooling layers. The
encoder reduces the signal’s temporal resolution by a factor of eight and
raises the number of output characteristics from a single aggregate power
consumption value to 256. The TP block provides context information to the
decoding block, allowing it to create extra features for decoding by
aggregating encoder output with various resolutions. The encoder output is
passed through four average pooling modules with various filter sizes, which
degrade the temporal resolution while maintaining the number of features,
before being convolutioned with a unit filter size. This reduces the number of
characteristics to one-quarter of those used in the input. The convolutional
results are used as the input for a rectified linear unit activation function,
which is then batch normalized. Lastly, linear up-sampling yields a temporal
resolution at the TP block’s output that is equal to the encoder’s output. A
dropout is also added to the output, allowing the network to be controlled.
The context features generated by this block are connected to the encoder’s
detail features, doubling the total number of features in the decoder’s input.
To raise the signal’s temporal resolution and lower the number of features,
the decoder contains a transposed convolutional layer with a stride and kernel
size of 8. The ReLU is still used as the activation function, followed by an
extra convolutional layer with an unified kernel size that keeps the temporal
resolution and increases the number of output channels to match the number of
devices being analyzed. In the output, a sigmoid function is used. This is
because in the semantic segmentation of photos, each pixel is connected to a
single class, however in the current application, many appliances might be in
use at the same time. While working in this manner, the network decomposes all
appliances at the same time. This should enable an encoder to use more broad
convolutive filters that aren’t specialized for a single kind of appliance,
boosting the neural network’s capacity to generalize. Gradient descent
optimization may be used to find the net’s weights. The loss function is a
binary crossentropy applied to each output channel that assesses the disparity
between the activations predicted by the net $\hat{a}_{i}(t)$ and the actual
ones ${a}_{i}(t)$ for each appliance under consideration and for each instant
of the period under consideration.

##  IV NILM Public Datasets

To develop NILM (non-intrusive load monitoring) algorithms and assess their
performance, the research community provides various NILM datasets in the
public domain [30]. Since each dataset monitors different appliances in
diverse environments and buildings over a varied time period, each dataset has
its own specific criteria [31, 32]. However, it has been observed that many
public datasets have structural variations that require pre-processing before
usage. To address this issue, the dsCleaner Python module was developed to
standardize, clean, and convert time series data into a consistent file
format, and also includes a resampling method for datasets. Typically, NILM
datasets consist of aggregated energy data from a single meter and the actual
energy consumption of each appliance, which is measured by plug-level meters
and serves as the ground truth for evaluating NILM algorithms. Table I lists
the most popular publicly accessible NILM datasets for research purposes [30].

TABLE I: Summary of NILM public datasets Dataset |  Sampling Rate Time Resolution | Duration | Type |  Number of Households |  Number of Appliances | Country |  URL
---|---|---|---|---|---|---|---
REDD |  16.5 KHz | 19 days | Residential |  6 |  6 | US |  https://tokhub.github.io/dbecd/links/redd.html
BLUED |  12 KHz | 1 week | Residential |  1 |  50 | US |  https://tokhub.github.io/dbecd/links/Blued.html
UK-DALE |  16 KHz | 2 years | Residential |  5 |  5 | UK |  https://jack-kelly.com/data/
REFIT |  8 seconds | 2 years | Residential |  20 |  9 | UK |  https://pureportal.strath.ac.uk/en/datasets/refit-electrical-load-measurements
AMPds2 |  1 min | 2 years | Residential |  1 |  21 | Canada |  http://dx.doi.org/10.7910/DVN/FIE0S4

In 2011, the Reference Energy Disaggregation Dataset (REDD) [32] became
available as the first openly accessible dataset designed specifically to aid
NILM research. Following this, the building-Level fully-labeled dataset for
Electricity Disaggregation (BLUED) [33] was released in 2012, which contained
data from a single household.

The Almanac of Minutely Power dataset (AMPds) [34], on the other hand, was
made public in 2013 and comprised both aggregate and sub-metered power data
from a single household. The Almanac of Minutely Power dataset Version 2
(AMPds2) [35] is another dataset that captures all three primary types of
consumption, including electricity, water, and natural gas, over an extended
period of 2 years. Furthermore, it provides 11 measurement characteristics for
electricity. The data in AMPds2 has been pre-cleaned to ensure consistent and
comparable accuracy results among researchers and machine learning algorithms.

The REFIT Electrical Load Measurements [36] dataset is another one that
includes cleaned electrical consumption data in Watts for 20 households at
both aggregate and appliance levels, timestamped and sampled at 8-second
intervals. It is designed to support research into energy conservation and
advanced energy services, ranging from non-intrusive appliance load
monitoring, demand response measures, tailored energy and retrofit advice,
appliance usage analysis, consumption and time-use statistics, and smart
home/building automation. Finally, the UK Domestic Appliance-Level Electricity
data set (UK-DALE) [37] was released, containing data from four households.

The available NILM datasets have varying sample rates ranging from 1 Hz to 100
kHz and cover individual appliances as well as residential complexes. While
data collected from individual appliances can be valuable for modeling and
training the NILM system, its performance may not be optimal when tested on
the entire residential building. Conversely, relying solely on whole-household
datasets may not be appropriate for training the algorithms, especially when
individual appliance data is unavailable.

Furthermore, certain databases provide primary current and voltage signals,
whereas others provide calculated electrical parameters such as active power,
reactive power, apparent power, and power factor. However, in order to create
an effective NILM system, it is crucial to obtain unprocessed electrical
signals in order to extract fundamental and harmonic characteristics.

##  V Experimental Results

###  V-A Evaluation Metrics

To evaluate the performance of algorithms in recognizing appliance switching
ON or OFF, the classification metrics presented in Eqs. 5-8 were used. The
metrics are calculated using True Positive (TP), True Negative (TN), False
Positive (FP), and False Negative (FN). TP represented the number of times a
device was correctly recognized as ON, whereas TN represented the number of
correctly identified OFF occurrences. FP highlighted instances when ON states
were recorded despite the appliance not consuming power. On the other hand, FN
displayed the number of OFF occurrences that were incorrectly recognized.

$\
\textnormal{Accuracy}=\frac{\textnormal{TP}+\textnormal{TN}}{\textnormal{TP}+\textnormal{TN}+\textnormal{FP}+\textnormal{FN}}$
(5)

$\
\textnormal{Presicion}=\frac{\textnormal{TP}}{\textnormal{TP}+\textnormal{FP}}$
(6)

$\
\textnormal{Recall}=\frac{\textnormal{TP}}{\textnormal{TP}+\textnormal{FN}}$
(7)

$\
F_{1}=2*\frac{\textnormal{Presicion}*\textnormal{Recall}}{\textnormal{Presicion}+\textnormal{Recall}}$
(8)

The recall metric in Eq. 7 measures the ratio of correctly identified positive
instances (TP) to the total number of positive instances in the dataset. On
the other hand, the precision metric in Eq. 6 represents the ratio of
correctly identified positive instances (TP) to the total number of instances
identified as positive by the algorithm. The $F_{1}$ score is a weighted mean
of precision and recall, which is used to determine the accuracy of the
algorithm in identifying appliance states. Higher $F_{1}$ scores indicate
better algorithm performance in recognizing appliance state transitions.

The mean absolute error (MAE) and proportion of energy correctly allocated
(PECA) metrics in Eqs. 9 and 10, respectively, are non-event-based metrics
used to evaluate the accuracy of load disaggregation systems in calculating
and assigning electricity usage. MAE measures the average absolute difference
between the estimated and actual energy usage, while PECA evaluates the
percentage of energy correctly allocated to individual appliances.

$\ \textnormal{MAE}=1/T*\sum_{t=1}^{T}|\hat{y}_{t}^{i}-{y}_{t}^{i}|$ (9)

$\
\textnormal{FECA}=1-\frac{\sum_{t=1}^{T}\sum_{i=1}^{N}|\hat{y}_{t}^{i}-{y}_{t}^{i}|}{2\sum_{t=1}^{T}\bar{y}_{t}}$
(10)

In the preceding equations, $\hat{y}_{t}^{i}$ and ${y}_{t}^{i}$ are the
estimated and ground-truth power of the $i^{th}$ device at time step t,
respectively. Furthermore, $\bar{y}_{t}$ is the total power at time t [24].

###  V-B Performance Evaluation and Comparison Study

In this part, the disaggregation findings for the NILM approaches using the
REDD, UK-DALE, and REFIT datasets are shown. The performance indicators
obtained by executing the tests on these datasets shows that $F_{1}$ produced
the best results for refrigerators, air conditioners, freezers, televisions,
and washing machines across the three datasets, with values greater than 0.70.
Toasters and electronics, on the other hand, have lower $F_{1}$ scores of
roughly 0.25 owing to misclassification caused by the non-uniform pattern of
these items.

The accuracy metrics of the findings were compared for published methods: on-
line NILM [27], NILM-TK [14], an FHMM implementation; Neural-NILM [7], a DNN
adaption for energy estimation. The Neural-NILM used three DNN architectures:
i) long short-term memory, ii) de-noising auto-encoders, and iii) rectangles.
Rectangle networks, in particular, regress the start-time, end-time, and
average power of appliance activation.

In experiments using UK-DALE dataset, a comparison of on-line NILM, NILM-TK
and Neural-NILM methods for five appliances (fridge, washing machine,
dishwasher, microwave, and kettle) is done. The microwave gives the lowest
marks for all three methods. MAE and an $F_{1}$ score of roughly 195 Watts and
0.01, respectively, are reported by NILM-TK. The best MAE of 6 Watts and an
$F_{1}$ score of 0.21 is shown by the neural-NILM. With an $F_{1}$ score of
about 0.35, the on-line NILM method outperforms the other two. The MAE and
$F_{1}$ scores reported by NILM-TK are roughly 67 watts and 0.55,
respectively. The Neural-NILM has an MAE of 18 Watts and an F1 score of 0.82.
In terms of energy estimate, the Neural-NILM outperformed the suggested
technique, particularly for complicated equipment like dishwashers and washing
machines. Nonetheless, the time and computational resources required to train
the neural network and generate the models need a large amount of appliance-
level data. The on-line NILM technique, on the other hand, may generate
appliance models using aggregate data without the necessity for appliance-
level sub-metered data.

TABLE II: Comparison of Non-Intrusive Load Monitoring (NILM) Methods Method |  Pros |  Cons |  Data Requirements |  Real-time Performance |  Interpretability |  Evaluation Performance
---|---|---|---|---|---|---
Template Matching |  Simple, easy to implement |  Requires pre-defined templates for each appliance, limited accuracy |  None |  Fast |  High |  Limited by pre-defined templates
Steady-State Analysis |  Accurate for steady-state loads |  Limited accuracy for transient loads, requires detailed knowledge of the power system |  Detailed power system data |  Fast |  Medium |  Limited by power system model
HMM and fractional HMM |  Can handle transient loads, can identify multiple appliances simultaneously |  Requires training data for each appliance, can be computationally expensive |  Labeled training data |  Slow |  Low |  Sensitive to initialization and noise
Deep Neural Networks |  High accuracy, can handle complex appliance behavior, can identify multiple appliances simultaneously |  Requires a large amount of training data, can be computationally expensive, limited interpretability |  Labeled training data |  Fast |  Low |  Dependent on quality and quantity of training data
Autoencoders |  Can handle variable appliance behavior, can identify multiple appliances simultaneously, can learn without labeled data |  Requires a large amount of training data, can be computationally expensive |  Unlabeled training data |  Fast |  Low |  Dependent on quality and quantity of training data

##  VI Conclusion

The three-phase distribution network often feeds the final residential
customers through single phase cables. It is important to make the loads
balanced. Demand side management and different optimisation techniques would
help on this. However, understanding the contribution of different appliances
to energy consumption is beneficial. NILM is demonstrated to be a good
approach to this end. The accuracy of NILM depends on the method applied. This
paper reviewed some deep learning-based methods which outperform other
existing NILM algorithms. The paper compared the results of applying these
advanced methods to provide a basis for future implementation. These datasets
have public access and are widely used in NILM literature. Several performance
criteria are formulated to analyze the performance of the methods.

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