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RESEARCH POSTER PRESENTATION DESIGN © 2012
www.PosterPresentations.com
Non-Intrusive Load Monitoring (NILM) is a set of
techniques which used to disaggregate the electrical
consumption of individual appliances from measured
voltage and/or current at a limited number of locations
of the power distribution system in a building.
The original idea of Nonintrusive load monitoring was
developed by George Hart at the Massachusetts Institute
of Technology in the 1980s. Recent technological
achievements e.g., smart meters as well as novel
algorithms and an overall interest in improving the
electrical grid have excited a new interest in this topic.
NILM has some challenges to detect multistate appliances
such as TV, small loads such as lamp, unknown duration
appliances such as PC, and new appliances such as
Treadmill. These challenges have opened a wide
interesting research area for academic groups.
INTRODUCTION
The proposed statistical method models the building
electrical system as a stochastic process. The random
variables of this process are status of each appliance,
which are unobservable for a person who is looking only
at the measured data at a power panel. Therefore,
appliances are modeled by latent states that only
observations of them (i.e. the total power consumption)
are available.
In order to model the system, a hidden Markov model
(HMM) is employed. HMM is the workhorse statistical
model for discrete time series with numerous application
in different fields of science and engineering such as
voice recognition.
After modeling the system, the proposed method tries to
estimate the ON/OFF status of each appliances, by
calculating the probability of the appliance being ON
given the measured power signal and the HMM model of
system. If the calculated probability is more than a
threshold, the appliance is ON; otherwise, the appliance
is OFF.
Statistical Pattern Recognition
Demand Side Management
Applications
Seyed Mostafa Tabatabaei, Kiarash Shaloudegi
Non-intrusive Load Monitoring Methods & Applications
Waveform Pattern Recognition
In another NILM research in PDS lab, two different
methodologies have been proposed which both of the
methods look into the whole appliance’s behavior in time
series instead of only studying changes in waveform.
The first method maps the time series into delay space
and use dynamic behavior of appliances to find their
patterns. In the next step, to detect the pattern of each
appliance in power signal and probable appliances
mixture, multi-label classification has been used.
The second method is a pattern search method which
searches in the power time series to find potential
appliance candidates; in order to match waveforms with
database, dynamic time warping similarity measure is
employed. In addition, wavelet transformation has been
used to reduce dimensionality of data without losing
information.
The methods are implemented on several datasets and
their performance are validated.
Monitoring total power consumption of a home and
breaking down power consumption to a certain level of
appliances, which could be a group of similar appliances
such as lights, motor driven appliances or consumption
detail of each appliance. The ideal case is, breakdown
power consumption to appliance level.
Feedback on energy consumption to the customers
with providing detail of each appliance usage on
electric bill.
Suggest cost-effective consumer appliances
replacement to both enhance federal and local market
economy
Sub-meter application, e.g., Electric vehicle charging
time monitoring
Energy Audit
Electric utilities install appliance controllers on
deferrable loads such as heating, ventilation, and air
conditioning (HVAC) to shed them during times of peak
power usage for smart grid applications. For example,
thermostats control allows the air conditioner to turned
on in advance of the temperature rising thus delivering
substantial savings and better comfort. NILM provides
required data.
Security Purposes
NILM could provide security for a long period vacant
home by monitoring activity inside the home, in order to
automatically generate a phone message to report
unusual appliance usage. If a security light burns out, or
garage-door openers are activated, etc., the owner will
be notified immediately.
Feeder Monitoring
Feeder monitoring helps to find cumulative pattern of
energy use for a chosen group of customers, such as
commercial or residential, or characteristics of
appliances. Applications are for demand side
management programs and miss behaviour detection.
Failure Analysis
NILM is an ideal platform for extracting useful
information about any system that uses
electromechanical devices. It is possible to use state and
parameter estimation algorithms to verify remotely the
healthy performance of electromechanical loads by using
NILM. Fault detection and diagnostics are based on duty
cycles, event and operational signatures, and unusual
power consumption. Applications will be able to forecast
imminent problems, deliver maintenance reminders, and
identify when professional service is needed.
In literature, a failed underground septic pump was
detected by its abnormally low power consumption. In
another application, a refrigerator which was on almost
all of the time was detected and replaced. In industrial
application for motor failure detection, a monitoring
method has been proposed. Failure analysis application
has been also applied to monitor shipboard systems
monitoring.
Miss-behaviour Detection
Electricity theft and illegal activities causes situations
where more power flows through the feeders than usual.
This creates power surges and system failures as well as
several safety hazards for people, property and power
system. By feeder monitoring, energy theft location can
be identified. For example, theft identification can be
used for marijuana growing detection through monitoring
consumer’s behaviours, those with distinct large
consumption pattern could be potential suspects for
illegal activities that needs huge amount of power.
Old patient activity monitoring
The activity recognition is an interesting area about
smart home; it provides a form of autonomy for
individuals who require increased daily monitoring such
as old patients where there is a growing demand for
technology that could aid in the care of elder people in
their own homes. This is actually a method for
determining the routine of a person.
Met
ho
ds Supervised
Event Based
Parametric
Maximum Likelihood
Correlation
Non-Parametric
Pattern Recognition
Optimization
Waveform Matching
Time Series
Statistical Model
Unsupervised
Feature Clustering
Clustering
One of the main research focuses in PDS lab are on
consumption monitoring which a group of PhD and master
students work to develop NILM: