Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Corresponding author: Adnan Anwar (e-mail<EMAIL_ADDRESS>
# A Comprehensive Review on the NILM Algorithms for Energy Disaggregation

AKRITI VERMA1  ADNAN ANWAR2  M. A. PARVEZ MAHMUD3  MOHIUDDIN AHMED4  and ABBAS
KOUZANI3  School of Information Technology, Deakin University, Geelong,
Australia  Strategic Centre for Cyber Security Research and Innovation (CSRI),
School of IT, Deakin University, Australia  School of Engineering, Deakin
University  School of Science, Edith Cowan University, Perth, Australia

###### Abstract

The housing structures have changed with urbanization and the growth due to
the construction of high-rise buildings all around the world requires end-use
appliance energy conservation and management in real time. This shift also
came along with smart-meters which enabled the estimation of appliance-
specific power consumption from the building’s aggregate power consumption
reading. Non-intrusive load monitoring (NILM) or energy disaggregation is
aimed at separating the household energy measured at the aggregate level into
constituent appliances. Over the years, signal processing and machine learning
algorithms have been combined to achieve this. Incredible research and
publications have been conducted on energy disaggregation, non-intrusive load
monitoring, home energy management and appliance classification. There exists
an API, NILMTK, a reproducible benchmark algorithm for the same. Many other
approaches to perform energy disaggregation have been adapted such as deep
neural network architectures and big data approach for household energy dis
aggregation. This paper provides a survey of the effective NILM system
frameworks and review the performance of the benchmark algorithms in a
comprehensive manner. This paper also summarizes the wide application scope
and the effectiveness of the algorithmic performance on three publicly
available data sets.

###### Index Terms:

Differential Privacy, Healthcare Systems, Internet of Things (IoT),
Intelligent Transport System (ITS), Industrial Internet of Things (IIoT),
Privacy Preservation, Smart Grid (SG)

=-15pt

##  I Introduction

There has been immense research on developing technological solutions in order
to address the energy requirements which are rising exponentially and thereby
making the challenge of energy conservation harder day by day. The increasing
energy demands not only affects a country’s economy but also comes with
significant negative implications on the environment. Hence, the only
effective way to conserve energy now is to encourage its efficient usage. A
fine-grained monitoring of energy consumption and communicating the same to
the consumers can help in the noteworthy reduction of energy wastage [1], [2].
The method for the disaggregation of electrical measurements was proposed by
Hart [3] at the beginning of the 1980s and owing to the shift towards smart
meters, ease of data availability in the last few years and amidst rising
interests in climate change led to the boost of extensive research in this
field. Energy disaggregation primarily aims to provide a piece of detailed
information about energy sensing and a breakdown of the consumption patterns,
which can further be analysed for the automation of energy management and the
development of similar systems. Strategies such as rescheduling the appliances
with high-power consumption rates to off-peak times will thus be feasible [4].
It would also help appliance design companies acquire a more reliable
understanding of the power-usage patterns of their devices and hence will
enable them to develop sustainable products. There are two primary approaches
to energy disaggregation, in particular, Intrusive Load Monitoring (ILM) and
Non-Intrusive Load Monitoring (NILM). Intrusive load monitoring consists of
using a low-end metering device for the measurement of the electricity
consumption of one or more appliances, usually employing at least one
censoring device per appliance, whereas in the case of NILM it only requires a
single meter per house or a building that is being monitored. Non-intrusive
load monitoring (NILM) or energy disaggregation is aimed at separating the
household energy measured at the aggregate level into constituent appliances.
The presence of nilmtk-contrib [5], an open-source, implementation of the
energy disaggregation problem, has unfolded the means for comparisons of the
different algorithms executing energy disaggregation. It has also enabled
researchers to observe and evaluate NILM approaches for abstraction and varied
evocation as they can be applied to multiple data sets accessible online. The
versatility of NILM API makes experimentation in this field easy by lowering
the entry barriers and making the implementation generic irrespective of the
datsets and algorithms which helped to progress research in this area.

TABLE I: Articles included in systematic literature review Subject |  Purpose |  Dia-logue on framework |  Dis-cussion on NILM-API |  Related dataset references |  Algo-rithm implementation |  NILM-API implementation |  Empir-ical analysis |  Future research prosp-ects
---|---|---|---|---|---|---|---|---
Non-Intrusive Load Monitoring System Framework and Load Disaggregation Algorithms: A Survey[6] |  presents a general NILM framework and reviews publicly available data sets. |  Yes |  Yes |  Yes |  No |  No |  No |  Yes
A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem[7] |  presents an overview of NILM, related methods and reviews state-of-the-art NILM algorithms. |  Yes |  Yes |  Yes |  No |  No |  No |  No
Building power consumption datasets: Survey, taxonomy and future directions[8] |  analyses the nature of building energy consumption datasets. |  Yes |  No |  Yes |  No |  No |  No |  Yes
On performance evaluation and machine learning approaches in non-intrusive load monitoring[9] |  determines the accuracy and generalisation abilities of NILM algorithms using the data sets REDD, UK-DALE, and Dataport. |  Yes |  Yes |  Yes |  No |  No |  No |  Yes
Prospects of Appliance Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review[10] |  encourages the incorporation of appliance-level e-monitoring and load disaggregation; sets requirements to implement load disaggregation in next-generation e-monitors. |  Yes |  No |  Yes |  No |  No |  No |  Yes
An Overview of Non-Intrusive Load Monitoring: Approaches, Business Applications, and Challenges[11] |  surveys NILM framework and load disaggregation algorithms; reviews load signature models, exhibits existing datasets and performance metrics. |  Yes |  Yes |  Yes |  Yes |  No |  Yes |  Yes
Literature Review of Power Disaggregation[12] |  reviews the current state of the algorithms and NILM systems. |  Yes |  No |  No |  No |  No |  No |  Yes
Load Disaggregation Technologies: Real World and Laboratory Performance[13] |  reviews recent field studies and laboratory tests of NILM technologies. |  Yes |  No |  No |  Yes |  No |  Yes |  Yes
Proposed Comprehensive review on energy disaggregation |  reviews of the state-of-the-art techniques, implements NILMTK, presents analysis and draws conclusions |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes

##  II Scope and contribution of this paper

There has been a good number of research and publications in energy
disaggregation, non-intrusive load monitoring, home energy management and
appliance classification. Recent discoveries in machine learning and computer
vision has driven a lot of attraction to machine learning-based strategies for
NILM. As it develops, energy consumption datasets serve as the basis for train
and test data for NILM algorithms. These data sets are generated by using a
piece of measurement equipment such as a smart plug and combing the energy
readings given by smart-meters and appliance-level meters. The increasing
popularity of research in this field has led to a large number of datasets
being released in the past few years. NILMTK was designed specifically as an
open-source implementation to unfold the means for comparisons and
proliferation of the different algorithms executing energy disaggregation.
[5]. Performance evaluation, contrast and comparison of different NILM
algorithms is still a research challenge, for multiple reasons [9] [14] [15].
Table-I is a tabular depiction of the literature review which integrates the
information about the purpose of research and their major findings in this
field, that has been compiled from similar survey papers. It can be observed
that the prospects of NILM application are both, wide and versatile. There
have been multiple attempts to devise the generalisation abilities of existing
NILM algorithms in order to incorporate the methodology to appliance-level
monitoring and disaggregation. It is also challenging to collect and store
data at the required sample rates in order to make data sets available for
these algorithms. This paper provides a concise and clear overview of the NILM
framework and the NILMTK-API by implementing the API on three publicly
available data sets and comparing the observed results.

This survey article exhibits the following concepts:

  1. 1.

This paper aims to present an updated overview of the techniques used in the
NILM framework including the recent developments in its strategies for the
energy disaggregation problem.

  2. 2.

The paper presents an experimental overview of the application of NILM-API,
which was released with nilmtk-contrib, on three publicly available data sets,
draws conclusions and highlights on future research directions.

  3. 3.

A detailed overview of the energy disaggregation problem is presented. Here,
we have shown the advantages of the NILM API which enables comparison of
algorithms in this domain even without expertise in the field.

##  III Organisation of this paper

The rest of the paper is organised as follows. We start with a brief
background on the load monitoring approaches which is followed by a formal
introduction to the energy disaggregation problem and a discussion about the
available implementations and their general framework. We then introduce the
State-of-the-art Disaggregation Techniques, the NILMTK-contrib repository, and
summarise the different methods that are being employed for NILM. Next, we
describe the benchmark data sets, including the features taken into
consideration for the experiment API. Later we, describe the advantages of
this implementation by the means of an empirical comparison, before we sum up
our analysis.

##  IV Background on Load Monitoring approaches

Load monitoring and identification is a mechanism for the evaluation and
regulation of the usage of electrical energy and operating conditions of
individual appliances, wherein the main power metre located in the house forms
the basis of the analysis of the composite load measurement. They can supply
the customer and the utility with information such as the type of load, the
detail of the electricity consumption and the operating conditions of the
appliances. The practice of load monitoring is quintessential for the
management of energy as it gives us statistical insights on appliance energy
consumption and their patterns which can be applied for scheduling heavy loads
and optimal utilisation of energy resources.

###  IV-A Intrusive load monitoring

Intrusive load monitoring consists of using a low-end metering device for the
measurement of the electricity consumption of one or more appliances. It is
said to be ’intrusive’ in order to indicate that the meter which is being used
to employ energy monitoring is located inside the dwelling, typically close to
the appliance that is monitored. In essence, an intrusive load monitoring
system standardises a mechanism for measuring the energy usage of an appliance
by employing a metering system that connects each appliance to power meters
within the household. It requires, therefore, entering the house and is thus
remarked as an intrusive system. The results obtained with intrusive load
monitoring are accurate, however, it requires an elaborate installation
incurring high costs which usually demands wiring and units for data storage
to the households concerned [16]. The techniques for load monitoring can also
be divided into direct or indirect monitoring techniques [17]. Direct
monitoring techniques are often referred to as physically intrusive
procedures, measure the electrical properties of the power of each appliance
requirement. The process involves a tiny low-end device that is connected to
the power cord of each device, to generate physically intrusive signatures for
measuring the energy consumed by the appliances. The state of operation of an
appliance is signalled to the data collecting device, whenever the appliance
is switched on. The electromagnetic field produced by the current flow through
the wire is used to calculate the power drawn by the appliance. Although it
provides an accurate measurement of energy consumption, it is not always
economically feasible [17].

###  IV-B Non-intrusive load monitoring

Non-intrusive load monitoring consists of measuring electricity consumption
using a smart meter, which is usually placed at the meter panel. It is also
known as one-sensor metering since the whole process relies on a single point
of measurement. This method qualifies to be non-intrusive meaning that no
extra equipment is installed inside the house. Since this is a one-sensor
metering procedure, the signatures of appliances get superposed in order to
comprehend the contribution of single appliances, they’re needed to be
separated. This operation of separating the energy drawn by each appliance is
called the disaggregation of the total electricity consumption. Non-intrusive
load monitoring is a simple and reliable way to determine energy usage and
therefore the status of operation of specific appliances, carrying the
analysis of the aggregate load estimated by the building’s main meter. The
process of NILM also involves the analysis of individual appliance energy
consumption, the deduction of the devices being utilised in a building as well
as the changes happening within the voltage and current entering into a
building [16]. It is considered non-intrusive since it does not involve an
intrusion into the building or consumer premises when measuring the energy
consumption of various appliances. Smart metres equipped with NILM technology
are used by companies to survey the particular uses of electrical power in
different homes. NILM is taken into account for the cost-effective alternative
to intrusive monitoring techniques. The ideation behind analysing the flow of
power in order to record the operating conditions of appliances in a household
was coined by George W. Hart while he attempted to collect and examine
residential load data as a part of a residential photo-voltaic system [3]. The
acknowledgement of step-change in either active or reactive power out of the
total load is the working principle for load monitoring. It is executed by
modifying the operating state of the various customer’s appliances. A NILM is
installed temporarily to analyse the characteristics of the appliances which
may be used to recommend ways for reducing energy demand and costs.

##  V The Energy Disaggregation Problem

Speeding urbanisation has led to some conventional changes in the housing
structures which engrossed the need for the development of high-rise buildings
all around the world. The maintenance of these buildings requires end-use
appliance energy conservation in a synchronous manner. The transformation in
housing patterns also came together with a mechanism for an appliance level
energy break down of the building’s aggregate power consumption. The
building’s total energy reading can now be used for the estimation of
appliance-specific power consumption which is facilitated by the use of smart
meters. Nearly, two decades ago, a method for the disaggregation of electrical
measurements was proposed by Hart, which required only the examination of the
overall load data in order to identify signatures of the power consumption of
each appliance among them. The proposed methodology did not require the
installation of any equipment inside the property of the customer and is thus
considered to be non-intrusive. The aggregated data for the building’s energy
usage can be obtained from outside the building or the residence using the
main electrical panel. The separation of the whole-house building data into
its principal energy-consumption constituents is the complete goal of this
process. NILM algorithms have the ability to determine device-specific energy
disaggregation using only the aggregated data collected from a single
measurement point and thus Non-Intrusive Load Monitoring remains an attractive
method for energy disaggregation.

###  V-A General Framework of NILM

As mentioned in the nilm toolkit [5] [18], the model on which NILM works is as
follows: For an observed time series of aggregate measurements
$Y=(Y_{1},Y_{2},...,Y_{T})$, where $Y_{t}\in R^{+}$ represents the active
power which is measured in Watt-hours or Watts using an electricity meter at
time $t$. This signal represents the sum of the total energy consumed by all
the constituent appliances present in a building. For the same scenario, the
building facility is assumed to have $I$ appliances, wherein this equation
$X_{i}=(x_{i1},x_{i2},...,x_{iT})$ where $x_{it}\in R^{+}$ is the energy-
signal description for each appliance. According to the disaggregation
algorithm, the main readings of the buildings can be expressed as the sum
total of the appliance signals along with $E_{t}$ where $E_{t}$ is an error
term. Thus, when given the aggregate measurement of $Y$, the goal of the
energy disaggregation problem is to retrieve the unknown signals $X_{i}$.

Figure 1: Energy dis aggregation

Figure-1 demonstrates one such scenario where a high-rise building H contains
multiple apartments which are all equipped with a smart meter (SMX, where X is
the apartment number) and the building itself has a smart-meter aggregator
(SM0) that is connected to the electricity service. This building can be
considered as a meter-group. If the electricity consumption data for this
building is collected and sampled at a given rate, then applying NILM to this
meter group will give us the appliance-wise energy consumption patterns for
this group.

##  VI State-of-the-art Dis aggregation Techniques

The method for the disaggregation of electrical measurements was proposed by
Hart [3] at the beginning of the 1980s. Owing to the efforts towards energy
conservation and emission reduction, there has been extensive research in this
field which also got boosted due to the rise in deployment of smart-meters
across the globe. There are more than 10 publicly available data sets from
across different geographies to support significant research activities. The
growing interest in this field has led researchers to try and implement
various algorithms for solving the energy disaggregation problem, involving
neural networks, big data, soft-computing and statistical methods as shown in
Figure-2.

Figure 2: State-of-the-art Energy dis aggregation techniques

Probabilistic procedures have been gaining popularity for energy consumption
modelling using a hidden Markov model (HMM) [19], [20], [21], [22]. The
problem has been approached by implementing both supervised and unsupervised
methodologies including some, where signal processing methods were adopted to
enable the use of appliance’s features for the purpose of disaggregation [23],
[24]. Soft-computing techniques have also been utilized for the purpose of
solving NILM by employing fuzzy clustering [25], [26] and [27]. Additionally,
factorization procedures that aim to leverage the low-rank composition of
energy consumption, also form a set of algorithms that are used to perform
energy disaggregation [28],[29], and [30].

Late in the year-2019, the open-source API for non-intrusive load monitoring
was released as a toolkit (NILMTK). This was aimed at facilitating the means
for comparisons of the different algorithms executing energy disaggregation
and was intended to serve as their source library, the data set parsers needed
to implement NILM, and as a reference benchmark for algorithm implementations.

The nilmtk-contrib which is the current code repository that contains the API
documentation and usage examples [31], lessens the amount of entry-level
expertise required for algorithm developers and paves a way for algorithmic
comparisons. It adds several functional enhancements to the API and eases its
installation process as the disaggregation API gets rewritten.

###  VI-A Neural Networks

Neural networks have been successfully applied to a number of load scenarios
and have achieved better scores in terms of accuracy as well as producing
generalization for unseen houses [32], [33]. The most successfully employed
neural network algorithms are Recurrent neural networks and Long Short Term
Memory. There also exists an RNN based approach for NILM on small power office
equipment [34].

####  VI-A1 DAE

Autoencoders are made of simplistic neural structures and share similarities
with principal component analysis. If an autoencoder is composed of a linear
activation within each of its layers, it is highly likely that the latent
variables present at the bottleneck of the encoder network (the smallest layer
in the network) might directly resemble to the principal components from PCA.
They basically are a set of unsupervised machine learning concepts that result
in reducing the inherent dimensionality of the projected data while preserving
its essential features and enable the data from a higher dimension to be
synthesized in a lower dimension using non-linear transformation. It comprises
of two functions, specifically, encoder and decoder. The process of data
compression and down-sampling the input-data into a fewer number of bits and
mapping it back to the latent space is performed by the encoder. The decoder
function then puts the input encoding to use, reconstructing the input and
mapping it to the latent space. The latent space is present at the bottleneck.
A special deep neural network design implemented to extract a particular input
component from the received noisy data is termed a denoising autoencoder. As
the name suggests, it subtracts the noise from a given input in order to
extract meaningful data. It forces the network to undergo overfitting on the
random noise by adding some amount of white noise to the input. The error
incurred is then compared to produce the required output. Removal of grain
from input images and the reverb of voice signals are some popular
applications of DAE. For its usage in NILM, the mains signal has been treated
as a noisy appliance-power signal and the mains signal is considered to be the
summation of the total power consumed by the target appliance and some
additional noise. Since DAE denoises one appliance at a time, it needs
multiple trained models for a group of appliances to be disaggregated.
Moreover, in this scenario, the DAE gets as input a window of the mains
readings (length of time-window remains fixed) and produces the induced
appliance consumption values for the same time-window as output. The
architecture of the network remains as was proposed in nilmtk-contrib [5].

####  VI-A2 RNN

Recurrent Neural Networks are a set of algorithms commonly used for time
series prediction, natural language processing, and other sequence processing
queries. It tackles the input elements one by one and retains all the
previously encountered states. The algorithm works by saving the output of a
state and feeding it back to the input layer which carries it for later
predictions. Therefore, every element of the model takes into consideration
not only the current inputs but also what it remembers from the preceding
elements. Retaining the memory of previous states gives it the ability to
learn a long-term dependency by processing a series of events and manipulate
the entire context at once in order to make a prediction. An RNN is similar to
a chain-like recurrent module of a neural network but also carries a layer of
memory cells. Holding onto information throughout the network leaves the RNNs
with a few disadvantages, as they bring in the problem of vanishing gradients
and exploding gradients. Since the gradients are the basic carriers of
information in the RNN, they can sometimes either accumulate excessive
information which may even contain error, or can become extremely small making
the parameter updates insignificant. A very high parameter update to the
neural model might weigh it down unnecessarily and remarkably small ones might
make it ineffective. The resolution of this challenge is accomplished by using
memory cells which can both, save a state and keep a carry to minimize
information loss during sequence processing. These cells are LSTMs (Long Short
Term Memory). It also has a chain-like structure but with multiple
communicating neural network layers which decide the amount of data that is
required to be retained, the significance of the data to be remembered and the
part of the memory cell that impacts the output at the given time-step.
Moreover, RNNs work well for sequential data as in the case of NILM, because
they allow neuron connections in the same layer of the neural network. The
employed RNN model is fed a sequence of mains readings as input and produces a
single value output gives is the power consumed by the appliance at test. The
network also utilizes some units of long short-term memory (LSTM) and stores
values in the built-in memory cells to overcome the problem of vanishing
gradients. The network architecture remains as was proposed in nilmtk-contrib
[5].

####  VI-A3 Sequence-to-Sequence

The sequence to sequence learning model is a deep learning concept that is
used to convert from one sequence to another. It contains an encoder RNN to
understand the input sequence and a decoder RNN to decode the thought vector
thereby constructing an output sequence. The process of condensing the input
into a vector is achieved by an encoder network which passes its output to the
decoder network where the vector is unfolded to form a new sequence. There’s a
set of RNNs that operate commonly between the encoder and the decoder in a
sequence-to-sequence model. At each step of recurrence in the encoder network,
a new word is fed to the input which gets utilized in the subsequent step by
the next state. Once the decoder receives the final state from the encoder,
applies a discrete probability distribution (to the input at each step) to
predict the output taking into consideration, a loss function. The model here
tries to map the sequence of mains reading to the target appliance sequence by
learning a regressive map between them. The seq2seq model uses a regression
expression which is defined by $x_{t}:t+W/1=f(Y_{t}:t+W/1,Q)+E_{t}$ where $f$
is a neural network. The architecture of the network and other hyperparameters
remain as they were proposed in nilmtk-contrib [5].

####  VI-A4 Sequence-to-Point

Sequence to point learning (seq2point) operates by modifying the received
network input to work as a mains window, while the output for the target
appliance shows up functioning at the midpoint of its window. The model
believes that the midpoint of the target appliance is meant to be correlated
with the received mains signal information, both before and after the point of
time when it occurs. This training technique can also be considered to be a
non-linear regression. The architecture of the network and other
hyperparameters remain as they were proposed in nilmtk-contrib [5].

####  VI-A5 OnlineGRU

Gated Recurrent Unit (GRU) is a new generation of Neural Networks that replace
the LSTM units with lightweight Gated Recurrent Units (GRU) thus lessening the
computational requirements while delivering the equivalent performance. They
also attempt to reduce redundancy by optimising the recurrent layer sizes, as
well as minimising the risk of a vanishing gradient. GRUs are equipped with an
internal mechanism called gates which are capable of regulating the flow of
information and provide an effective solution to short-term memory. These
gates work by passing information on to the long chain of sequences as they
learn which part of the data is important. GRUs are similar to LSTMs except
that they do not have a cell state and instead, use the hidden state for
information transfer. They usually contain two gates, one for reset and update
each. The reset gate determines how much of the past knowledge to forget
whereas the update gate decides what part of the information to be thrown away
and what new information to be added to the unit. If the reset gate approaches
zero, the hidden state is forced to disregard its state and gets reset with
the current input. This permits the hidden state to discard any information
that’s found to be insignificant within the future. This result permits a more
compact representation. Whereas the upgrade gate controls how much information
from the past hidden state will be exchanged to the current hidden state. The
actuation of the GRU at a specific time may be a straight addition between the
past actuation and the candidate activation, where an updated door chooses how
much the unit overhauls its activation or content. The Online GRU model for
NILM, receives as input, the latest available mains readings of the test
appliance $Y_{t}:t+W/1$ and outputs the calculated power consumption
$x_{j}(t+W/1)$ of the appliance at test $j$, for the last point in time. The
architecture of the network and other hyperparameters remain as they were
proposed in nilmtk-contrib [5].

###  VI-B Big Data

A good deal of big-data approaches has popped for energy disaggregation with
the application of data analytics in smart meters [35] such as Neighbourhood
NILM which works on the intuition that ‘similar’ homes have ‘similar’ energy
consumption on a per-appliance basis [36] and a three-level learning framework
that would provide different levels of knowledge abstractions for smart
cities. It is scalable and also suits the nature of data generated by smart
cities which can become hierarchical [37].

###  VI-C Soft Computing

Non-intrusive load monitoring for residential electrical consumption has also
seen success in feature extraction and pattern recognition using fuzzy logic.
A set of soft-computing routines are being applied to learn and identify the
power usage patterns of different devices from aggregated consumption records
[25]. A fuzzy classifier has been proposed which can recognise the distinctive
appliance-energy states, such as energizing and de-energizing with the use of
Fuzzy C-Means (FCM) clustering and optimization [26].

###  VI-D Bayesian Procedures

Bayesian algorithms are being used for event detection and classifying the
load data into the individual contribution of appliances. Findings for event
detection method based on cepstrum smoothing [38] and LSTM models [39] along
with a set of modules to address the issues of multi-dimensionality when the
number of appliances increase [40] have been devised.

##  VII Data sets and Algorithms

The following section elaborates on the data sets that have been used for the
purpose of this research experiment and the algorithms that were implemented
using the NILM-TK API. The API was implemented using five neural network
algorithms on three publicly available data sets.

Figure 3: Predicted energy consumption: IAWE

###  VII-A Data sets Considered

This analysis uses three data sets, namely REDD, UK-DALE and IAWE. The data
sets used for this experiment have been taken from open-source locations.

####  VII-A1 REDD

The Reference Energy Disaggregation Data Set (REDD), is a freely available
data set containing detailed power usage information from several homes, which
has been published with the aim of promoting further research on energy
disaggregation. REDD was the first public energy dataset that was released by
MIT in 2011. REDD contains high and low-frequency energy readings from 6
households in the USA recorded for short period (between a few weeks and a few
months). This data set is widely used for the evaluation of NILM algorithms.

####  VII-A2 UK-DALE

UK-DALE is an open-access data set from the UK, that records Domestic
Appliance-Level Electricity at a sample rate of $16kHz$ for the whole-house
and at $1/6Hz$ for individual appliances. The data set contains 16 kHz current
and voltage aggregate meter readings and 6 second sub-metered power data from
individual appliances across 3 UK homes, as well as 1 second aggregate and 6
second sub-metered power data for 2 additional homes. An update to this data
set was released in August 2015 which has expanded the data available for
house 1 to 2.5 years. The updated data set has been utilized for this
experiment.

TABLE II: MAE against Ground Truth for Iawe MAE- Iawe | WindowGRU | RNN | DAE | Seq2Point | Seq2Seq
---|---|---|---|---|---
washing machine | 66.726196 | 65.665489 | 65.275597 | 65.736549 | 65.298805
fridge | 53.850956 | 68.835884 | 51.917274 | 36.775997 | 54.116047
computer | 24.112394 | 24.148933 | 24.46452 | 24.64801 | 24.25812
air conditioner | 169.634262 | 139.516449 | 139.706543 | 120.483185 | 141.557159
television | 1.347255 | 1.353503 | 1.347071 | 1.475708 | 1.449378
TABLE III: MAE against Ground Truth for REDD MAE- Redd | WindowGRU | RNN | DAE | Seq2Point | Seq2Seq
---|---|---|---|---|---
washing machine | 49.6175 | 60.661507 | 49.541073 | 47.514114 | 52.302326
fridge | 59.622471 | 68.384491 | 63.540127 | 55.803658 | 63.520336
light | 30.128523 | 32.435764 | 31.222496 | 31.245703 | 31.557386
sockets | 0.870423 | 0.888707 | 0.939906 | 0.827897 | 0.883221
microwave | 28.598331 | 28.599768 | 34.998123 | 21.999201 | 24.803219
TABLE IV: MAE against Ground Truth for UKdale MAE-UKdale | WindowGRU | RNN | DAE | Seq2Point | Seq2Seq
---|---|---|---|---|---
washing machine | 50.239201 | 55.136917 | 53.854832 | 49.03191 | 43.448345
fridge | 44.169342 | 44.513477 | 45.257683 | 44.960304 | 44.8424
light | 33.446453 | 34.21154 | 35.531647 | 33.719086 | 35.57534
dish washer | 49.290272 | 47.874489 | 39.505829 | 49.656605 | 41.864983
microwave | 21.169703 | 16.606073 | 25.805212 | 20.247181 | 19.278296

####  VII-A3 IAWE

The IAWE data set was released by the Indraprastha Institute of Information
Technology, India. This data contains the aggregated and sub-metered
electricity and gas readings from 33 household sensors, captured at a
resolution of one second. The data set covers 73 days of a single house in
Delhi, India.

###  VII-B Algorithms Considered

For the purpose of this experiment, five neural network based algorithms have
been utilised, namely: DAE, RNN, Sequence-to-Sequence, Sequence-to-Point and
OnlineGRU, all of which have been described in detail in section 4.1 of this
article.

Figure 4: Predicted energy consumption: REDD

###  VII-C Experimental Scenario

The tests were run on a machine with GeForce GTX 1660 Ti/PCIe/SSE2 GPUs with
Intel® Core™ i7-9750H CPU @ 2.60GHz × 12 and 16 GB RAM. The batch size was 32,
for all neural algorithms and they were trained for 5 epochs each. The sample
period was kept as 60 seconds.

###  VII-D Results and Discussion

For each data set, the network was trained for a period of 30 days based on
the data and tested on the subsequent 20 days using the nilmtk-contrib API to
predict the energy consumed per appliance and report the mean absolute
error(MAE) in each case. The outputs from the tests on the three datasets
under consideration do not collectively reveal the best performer out of the
five algorithms used. Moreover, there is no clear winner in each case except
UK-dale where Sequence-to-Sequence leads. This could be due to the differences
in the data used. Mean absolute error calculates the total magnitude of error
in a set of predictions, not considering their direction. It assumes that all
individual differences are supposed to have equal weight and therefore, for a
test sample, it gives out the absolute differences between the actual and
predicted observation. It can be used to represent the average prediction
error in terms of the variable at the test. The metric ranges from 0 to
$\infty$ and is unresponsive to the direction of errors. This score has a
negative orientation, the lower the value, the better. It effectively
describes the magnitude of residuals and does not indicate the
underperformance or overperformance of the model. All elements are given equal
weightage and hence contribute directly to the total value of the error and
larger the value, linear is the contribution.

Figure 5: Predicted energy consumption: UKdale

It is noticeable from Table-LABEL:Table:_IAWE that there is no clear winner in
the case of iAWE and the mean absolute errors are very high which could be
because the data set is based on a single housing facility. The mean absolute
errors remain similar in cases of washing machine, computer and television
with the reported errors being the minimum for television. The case of air
conditioner reports the maximum observed error from all three scenarios which
is greater than 100 for all five algorithms and varies largely for every
algorithm. The performance of Sequence-to-Sequence and DAE remains similar
across all five appliances with DAE performing the best for the television and
Sequence-to-Sequence performing the best for the washing machine. It can also
be observed from Figure-3, the difference in the load cycles of different
appliances.

It is evident from Table-LABEL:Table:_REDD that none of the algorithms in
consideration perform the best overall in the case of REDD although Sequence-
to-Point leads in four of the five most used appliances. This could be due to
the dissimilar usage trends of the appliances that the graphs clearly
demonstrate in Figure-4, hence, signifying the energy consumption patterns of
the appliances. The mean absolute errors are the lowest in this case making it
a preferred data set to base our conclusions on.

The mean absolute errors in for the sockets remain less than 1 which is the
minimum error reported by an algorithm in all three scenarios. The errors show
maximum variation in the case of the washing machine followed by the fridge.
There is little variation in the reported values of mean absolute error for
the sockets which is again followed by the light. The performance of WindowGRU
and RNN is comparable across light, sockets and microwave but differ largely
in cases of the washing machine and fridge.

Table-LABEL:Table:_UKdale shows that Sequence-to-Sequence performs the best
for most cases of UK-Dale and the mean absolute errors, although high, are
similar for nearly all appliances. The predictions from the graph indicate how
some appliances have clearly defined usage patterns (as seen in Figure-5)
whereas others are used on a daily basis. The mean absolute error stays the
lowest in the case of microwave with 16.60 being the minimum as reported by
RNN. It can also be noticed that the mean absolute error for the fridge
remains similar across all five algorithms followed by little variation in the
case of light and shows the maximum variation for the washing machine.

Different appliances use different quantities of energy and, thus, compared to
a high energy consumption load, the errors measured relative to low energy use
might be less significant. Furthermore, some end devices operate less
frequently than others, so if a statistically substantial number of run times
is not recorded, metric results may not be indicative of efficiency. In view
of these problems, it is recommended that every metric assessment reflects
some leveling of the use of energy or other basis for appropriate comparisons
of results across different end uses which coule be a fixed energy usage per
end use, a fixed number of real events per end use, or a fixed period that is
capable of capturing a variety of conditions.

##  VIII Conclusion and future work

In this paper, we demonstrated how nilmtk-contrib provides an interface to
energy disaggregation problems and also the advantages of the NILM API which
enables comparison of algorithms in this domain even without expertise in the
field, thus enabling efficient experimental evaluations. Consequently, acing
development within the field and supporting the progress of research in NILM.

Our experimental results carried out on three publicly available data sets
namely IAWE, REDD and UKdale do not indicate the presence of any patterns in
the output. This could be a result of the large differences in the data sets.
Although, it suggests that the API can be used for data sets from different
geographies.

With the need for energy and resources rising each day, careful and efficient
utilisation is the only way, to conserve it and energy dis-aggregation can be
an essential element in the conservation of energy since it elaborates the
energy usage tendencies. The trends observed in the energy-usage patterns from
a household can be used for the purpose of security as an anomaly in them
might represent a sign of appliance failure or illegal use of supplied
electricity. The appliance usage patterns can also be used to calculate and
control the amount of carbon emissions.

## References

  * [1] L. Shorrock, J. Utley _et al._ , _Domestic energy fact file 2003_. Citeseer, 2003.
  * [2] S. Darby _et al._ , “The effectiveness of feedback on energy consumption,” _A Review for DEFRA of the Literature on Metering, Billing and direct Displays_ , vol. 486, no. 2006, p. 26, 2006.
  * [3] G. W. Hart, “Nonintrusive appliance load monitoring,” _Proceedings of the IEEE_ , vol. 80, no. 12, pp. 1870–1891, 1992.
  * [4] A. Zoha, A. Gluhak, M. A. Imran, and S. Rajasegarar, “Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey,” _Sensors_ , vol. 12, no. 12, pp. 16 838–16 866, 2012.
  * [5] N. Batra, R. Kukunuri, A. Pandey, R. Malakar, R. Kumar, O. Krystalakos, M. Zhong, P. Meira, and O. Parson, “Towards reproducible state-of-the-art energy disaggregation,” in _Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation_ , ser. BuildSys ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 193–202. [Online]. Available: https://doi.org/10.1145/3360322.3360844
  * [6] M. Sun, F. M. Nakoty, Q. Liu, X. Liu, Y. Yang, and T. Shen, “Non-intrusive load monitoring system framework and load disaggregation algorithms: A survey,” in _2019 International Conference on Advanced Mechatronic Systems (ICAMechS)_ , 2019, pp. 284–288.
  * [7] A. Faustine, N. H. Mvungi, S. Kaijage, and K. Michael, “A survey on non-intrusive load monitoring methodies and techniques for energy disaggregation problem,” _arXiv preprint arXiv:1703.00785_ , 2017.
  * [8] Y. Himeur, A. Alsalemi, F. Bensaali, and A. Amira, “Building power consumption datasets: Survey, taxonomy and future directions,” _Energy and Buildings_ , vol. 227, p. 110404, 2020. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S037877882030815X
  * [9] C. Klemenjak, “On performance evaluation and machine learning approaches in non-intrusive load monitoring,” _Energy Informatics_ , vol. 1, no. 1, pp. 391–395, 2018.
  * [10] A. U. Haq and H.-A. Jacobsen, “Prospects of appliance-level load monitoring in off-the-shelf energy monitors: A technical review,” _Energies_ , vol. 11, no. 1, p. 189, 2018.
  * [11] M. Zhuang, M. Shahidehpour, and Z. Li, “An overview of non-intrusive load monitoring: Approaches, business applications, and challenges,” in _2018 International Conference on Power System Technology (POWERCON)_. IEEE, 2018, pp. 4291–4299.
  * [12] L. Jiang, J. Li, S. Luo, J. Jin, and S. West, “Literature review of power disaggregation,” in _Proceedings of 2011 International Conference on Modelling, Identification and Control_. IEEE, 2011, pp. 38–42.
  * [13] E. T. Mayhorn, G. P. Sullivan, J. M. Petersen, R. S. Butner, and E. M. Johnson, “Load disaggregation technologies: real world and laboratory performance,” _Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Tech. Rep. PNNL-SA-116560_ , 2016.
  * [14] L. Pereira and N. Nunes, “Performance evaluation in non-intrusive load monitoring: Datasets, metrics, and tools—a review,” _Wiley Interdisciplinary Reviews: data mining and knowledge discovery_ , vol. 8, no. 6, p. e1265, 2018.
  * [15] J. R. Herrero, Á. L. Murciego, A. L. Barriuso, D. H. de La Iglesia, G. V. González, J. M. C. Rodríguez, and R. Carreira, “Non intrusive load monitoring (nilm): A state of the art,” in _International Conference on Practical Applications of Agents and Multi-Agent Systems_. Springer, 2017, pp. 125–138.
  * [16] E. Aladesanmi and K. Folly, “Overview of non-intrusive load monitoring and identification techniques,” _IFAC-PapersOnLine_ , vol. 48, no. 30, pp. 415–420, 2015.
  * [17] M. Zeifman, K. Roth, and J. Stefan, “Automatic recognition of major end-uses in disaggregation of home energy display data,” in _2013 IEEE International Conference on Consumer Electronics (ICCE)_ , 2013, pp. 104–105.
  * [18] N. Batra, J. Kelly, O. Parson, H. Dutta, W. Knottenbelt, A. Rogers, A. Singh, and M. Srivastava, “Nilmtk: An open source toolkit for non-intrusive load monitoring,” in _Proceedings of the 5th International Conference on Future Energy Systems_ , ser. e-Energy ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 265–276. [Online]. Available: https://doi.org/10.1145/2602044.2602051
  * [19] J. Z. Kolter and M. J. Johnson, “Redd: A public data set for energy disaggregation research,” in _Workshop on data mining applications in sustainability (SIGKDD), San Diego, CA_ , vol. 25, no. Citeseer, 2011, pp. 59–62.
  * [20] O. Parson, S. Ghosh, M. Weal, A. Rogers, and N.-I. L. M. U. Prior, “Models of general appliance types,” 2012.
  * [21] M. Zhong, N. Goddard, and C. Sutton, “Latent bayesian melding for integrating individual and population models,” in _Advances in neural information processing systems_ , 2015, pp. 3618–3626.
  * [22] ——, “Signal aggregate constraints in additive factorial hmms, with application to energy disaggregation,” in _Advances in Neural Information Processing Systems_ , 2014, pp. 3590–3598.
  * [23] K. He, L. Stankovic, J. Liao, and V. Stankovic, “Non-intrusive load disaggregation using graph signal processing,” _IEEE Transactions on Smart Grid_ , vol. 9, no. 3, pp. 1739–1747, 2018.
  * [24] Y. Jin, E. Tebekaemi, M. Berges, and L. Soibelman, “A time-frequency approach for event detection in non-intrusive load monitoring,” in _Signal Processing, Sensor Fusion, and Target Recognition XX_ , vol. 8050. International Society for Optics and Photonics, 2011, p. 80501U.
  * [25] C. Puente, R. Palacios, Y. González-Arechavala, and E. F. Sánchez-Úbeda, “Non-intrusive load monitoring (nilm) for energy disaggregation using soft computing techniques,” _Energies_ , vol. 13, no. 12, p. 3117, 2020.
  * [26] Y.-H. Lin, M.-S. Tsai, and C.-S. Chen, “Applications of fuzzy classification with fuzzy c-means clustering and optimization strategies for load identification in nilm systems,” in _2011 IEEE international conference on fuzzy systems (FUZZ-IEEE 2011)_. IEEE, 2011, pp. 859–866.
  * [27] P. Shenavar and E. Farjah, “Novel embedded real-time nilm for electric loads disaggregating and diagnostic,” in _EUROCON 2007-The International Conference on” Computer as a Tool”_. IEEE, 2007, pp. 1555–1560.
  * [28] N. Batra and Y. Jia, “Transferring decomposed tensors for scalable energy breakdown across regions,” in _The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)_ , 2018.
  * [29] N. Batra, H. Wang, A. Singh, and K. Whitehouse, “Matrix factorisation for scalable energy breakdown,” in _Proceedings of the… AAAI Conference on Artificial Intelligence_ , 2017.
  * [30] J. Z. Kolter, S. Batra, and A. Y. Ng, “Energy disaggregation via discriminative sparse coding,” in _Advances in Neural Information Processing Systems_ , 2010, pp. 1153–1161.
  * [31] N. Batra, R. Kukunuri, A. Pandey, R. Malakar, R. Kumar, O. Krystalakos, M. Zhong, P. Meira, and O. Parson, “Towards reproducible state-of-the-art energy disaggregation,” in _Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation_ , 2019, pp. 193–202.
  * [32] J. Kelly and W. Knottenbelt, “Neural nilm: Deep neural networks applied to energy disaggregation,” in _Proceedings of the 2nd ACM international conference on embedded systems for energy-efficient built environments_ , 2015, pp. 55–64.
  * [33] W. He and Y. Chai, “An empirical study on energy disaggregation via deep learning,” in _2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)_. Atlantis Press, 2016, pp. 338–342.
  * [34] M. . T. E. Astal, M. Kalloub, A. M. Abu-Hudrouss, and G. Frey, “Office appliances identification and monitoring using deep leaning based energy disaggregation for smart buildings,” in _IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society_ , 2020, pp. 1986–1991.
  * [35] Y. Zhang, T. Huang, and E. F. Bompard, “Big data analytics in smart grids: a review,” _Energy informatics_ , vol. 1, no. 1, pp. 1–24, 2018.
  * [36] N. Batra, A. Singh, and K. Whitehouse, “Neighbourhood nilm: A big-data approach to household energy disaggregation,” _arXiv preprint arXiv:1511.02900_ , 2015.
  * [37] M. Mohammadi and A. Al-Fuqaha, “Enabling cognitive smart cities using big data and machine learning: Approaches and challenges,” _IEEE Communications Magazine_ , vol. 56, no. 2, pp. 94–101, 2018.
  * [38] L. De Baets, J. Ruyssinck, C. Develder, T. Dhaene, and D. Deschrijver, “On the bayesian optimization and robustness of event detection methods in nilm,” _Energy and Buildings_ , vol. 145, pp. 57–66, 2017.
  * [39] R. Jones, C. Klemenjak, S. Makonin, and I. V. Bajić, “Stop: Exploring bayesian surprise to better train nilm,” in _Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring_ , 2020, pp. 39–43.
  * [40] M. Kaselimi, N. Doulamis, A. Doulamis, A. Voulodimos, and E. Protopapadakis, “Bayesian-optimized bidirectional lstm regression model for non-intrusive load monitoring,” in _ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2019, pp. 2747–2751.

|  Akriti Verma is a postgraduate student studying Applied Artificial
Intelligence at Deakin University, Australia. Previously she has worked as a
Researcher at Tata Consultancy Services, India. She completed her graduation
in Information Technology from AKTU, India. She is driven by her interest in
analysis which when incorporated with AI principles enables her to understand
and engineer solutions for real-life use cases. She wishes to pursue research
in the same direction.
---|---
|  Adnan Anwar is a Lecturer and Deputy Director of postgraduate cybersecurity
studies at the School of Information Technology. Previously he has worked as a
Data Scientist at Flow Power. He has over 8 years of research, and teaching
experience in universities and research labs including NICTA, La Trobe
University, and University of New South Wales. He received his PhD and sMaster
by Research degree from UNSW. He is broadly interested in the security
research for critical infrastructures including Smart Energy Grid, SCADA
system, and application of machine learning and optimization techniques to
solve cyber security issues for industrial systems. He has been the recipient
of several awards including UPA scholarship, UNSW TFR scholarship, best paper
award and several travel grants including ACM and Postgraduate Research
Student Support (PRSS) travel grants. He has authored over 40 articles
including high-impact journals (mostly in Q1), conference articles and book
chapters in prestigious venues. He is an active member of IEEE for over 9
years and serving different committees.
---|---
|  M. A. PARVEZ MAHMUD received his B.Sc. degree in Electrical and Electronic
Engineering and Master of Engineering degree in Mechatronics Engineering.
After the successful completion of his Ph.D. degree with multiple awards, he
worked as a Postdoctoral Research Associate and Academic in the School of
Engineering at Macquarie University, Sydney. He is currently an Alfred Deakin
Postdoctoral Research Fellow at Deakin University. He worked at World
University of Bangladesh (WUB) as a ‘Lecturer’ for more than 2 years and at
the Korea Institute of Machinery and Materials (KIMM) as a ‘Researcher’ for
about 3 years. His research is focused on Energy Sustainability, Secure Energy
Trading, Microgrid Control and Economic Optimization, Machine Learning, Data
Science, and Micro/nanoscaled Technologies for Sensing and Energy Harvesting.
He accumulated experience and expertise in machine learning, life cycle
assessment, sustainability and economic analysis, materials engineering,
microfabrication, and nanostructured energy materials to facilitate
technological translation from the lab to real-world applications for the
better society. He has produced over 50 publications, including 1 authored
book, 3 Book Chapters, 29 Journal Papers, and 21 fully refereed Conference
Papers. He received several awards including “Macquarie University Highly
Commended Excellence in Higher Degree Research Award 2019”. He was involved in
teaching engineering subjects in the Electrical, Biomedical and Bechatronics
Engineering courses at the School of Engineering, Macquarie University for
more than 2 years. Currently, he is involved in the supervision of 6 PhD
students at Deakin University. He is a key member of Deakin University’s
Advanced Integrated Microsystems (AIM) research group. Apart from this, he is
actively involved with different professional organizations, including
Engineers Australia and IEEE.
---|---
|  Mohiuddin Ahmed is currently working as a Lecturer with the Academic Centre
for Cyber Security Excellence, Edith Cowan University, Australia. His research
intersects between data analytics and cybersecurity. His research has a high
impact on data and security analytics, false data injection attacks, and
digital health. Mohiuddin has led edited multiple books and contributed
articles in The Conversation. He has over 50 publications in reputed venues.
Mohiuddin secured the prestigious ECU Early Career Researcher Grant for
investigating effectiveness of blockchain for dependable and secure e-health.
He also secured several external and internal grants within a very short time
frame. He is a Senior Member of IEEE and member of Australian Computer
Society/certified professional.
---|---
|  Prof. Abbas Kouzani received his B.Sc. degree in Computer Engineering from
Sharif University of Technology, Iran, his M.Eng.Sc. degree is Electrical and
Electronic Engineering from University of Adelaide, Australia, and his Ph.D.
degree in Electrical and Electronic Engineering from Flinders University,
Australia. He was a lecturer with the School of Engineering, Deakin
University, and then a Senior Lecturer with the School of Electrical
Engineering and Computer Science, University of Newcastle, Australia.
Currently, he is a Professor with the School of Engineering, Deakin
University, Australia. He provides research leadership in embedded, connected,
and low-power devices, circuits, and instruments that incorporate sensing,
actuation, control, wireless transmission, networking and IoT, data
acquisition/storage/analysis, AI, energy harvesting, power management, and
fabrication for tackling research questions relating to a variety of
disciplines including healthcare, ecology, mining, infrastructure, automotive,
manufacturing, energy, utilities, and agriculture. Has produced over 370
publications including 1 book, 17 Book Chapters, 180 Journal Papers, and 181
fully refereed Conference Papers. He has 3 patents and 2 pending patents. He
has been involved in over $15 million research grants, and has managed
projects and delivered research solutions to over 25 Australian and
International companies. He received several awards including “Outstanding
Contribution to Scholarly Publication Award”, School of Engineering, Deakin
University, 2019. He has supervised 24 research fellows/assistants, and
produced 28 Ph.D. and 6 Masters by Research completions. Currently, he is
involved in supervision of 12 PhD students. He is the Director of Deakin
University’s Advanced Integrated Microsystems (AIM) research group.
---|---