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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: