When the load signature is properly selected, the non-intrusive load monitoring system
normally requires an event detection mechanism and a disaggregation algorithm to process
and analyse the sampled signatures and then figure out the exact on-off states of each
individual appliances.
Generally, most of the existing non-intrusive monitoring approaches can be grouped as event
based and non-event based. The event based method monitors the adopted signatures
continuously and assumes the operations of loads remain unchanged if no event is detected.
Normally, an edge detection algorithm or a threshold of signature is applied in this type of
method. Once an event is detected, the change of signature is captured and treated as
operation change of a certain appliance. Finally, this change of signature is classified through
different approaches, for instance, support vector machine, K-Nearest Neighbour and
Euclidean distance metric, with the database including the signatures of all appliances. The
appliance with closest signature is found and regarded as the one changing its operation (turn
on-off, mode changes). Some examples of previous work based on event detection are listed
in Table 2-1:
Table 2-1 Examples of event based approaches
Approaches Signature(s) Algorithm(s)
(Patel et al. 2007) Transient noise FFT Euclidean distance metric
50 (Figueiredo et al.
2011)
Real and reactive power, power factor
K-Nearest Neighbour, Support vector machine
(Wang & Zheng 2012)
Real power Clustering, Euclidean distance metric
(Wang et al. 2013) Current Envelope Euclidean distance metric
(Hassan et al. 2014) Wave shape Support vector method
Different from the above methods, non-event based method doesn’t have a stage detecting
events. All the samples of measurement are considered and processed for load disaggregation.
This type of method pays more attention on the relationship of signature over a relative long
time period and highly relies on the inference algorithm with high accuracy and robustness
rather than the diversity of signatures. One of the most popular approaches is to model the
appliances by using a single probabilistic framework, for instance Hidden Markov Model. In
this method, the following elements are defined to describe the behaviour of an appliance:
States: indicate the operation state of an appliance. It could be on-off states or operating modes.
Transition probability: indicate the probability of an appliance transit from one state to another. It is normally in matrix form and requires training to get it accurate.
Observations: indicate the observation or measurement of an appliance; normally it is real power value. Each state is deemed to emit observation varying with a certain
range.
Emission probability: indicate the probability of a state emit a certain observation and is described by a Gaussian distribution. It is in matrix form and requires pre-
known data for training.
For example, a kettle has two states, namely on and off. The probabilities of transition
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is switched on for one working cycle. The observations are the power value apparently; on-
state emits large power observation within a small range while off-state emits almost zero
power. With a known sequence of observed real power value, the states of the kettle can be
inferred by apply an inference algorithm, for instances, Viterbi Algorithm introduced in (H.
Kim et al. 2011).
Kotler provide a public dataset for load disaggregation research and firstly apply this method
on non-intrusive monitoring method in (Kolter & Johnson 2011). Different extension of
Hidden Markov model are then proposed and investigated in (Zia et al. 2011; Zoha et al.
2013; Mueller et al. 2014). However, those methods are evaluated by using public dataset and
not real implementation is achieved.
Compared with non-event based methods, event based methods requires less computation and
analysis since only detected events is focused for inference and identification. Furthermore,
event based methods have the potential to give the feedback of the operation of appliances
instantly while non-event based methods have to take advantage of the data over a long time
period. Therefore, event based methods is much more suitable for developing an online non-
intrusive load monitoring system which monitors the on-off states of each individual
appliances continuously.
2.2.4 Summary
It is noticed that the major difference of existing non-intrusive load monitoring methods are
the load signature they used. The disaggregation algorithms of different methods are all
among those several choices. Therefore, developing a new signature is essential for non-
intrusive load monitoring system. Based on the reviews of load signatures identified by
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such as real and reactive power, is very limited. Therefore, signature captured with high
sampling rate is preferred to be investigated in this project.
Considering computation efficiency of the disaggregation algorithm, event based method is
apparently the preferred choice over non-event based method in this project. The event
detection is performed on every sample but the inference and identification are only
conducted when an event is detected.
The ideal algorithm is required to be simple, computational efficient, robust and accurate.
Generally, processing signatures in numerical form, such as power and harmonics, requires
less computation compared with processing signatures in graphical or geometrical form, such
as envelope and trajectory. Built upon previously published work as reviewed in the chapter,
the project aims to develop a distinctive load signature in numerical form and propose a
simple and efficient algorithm to accomplish non-intrusive load monitoring in real time for
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