IEEE presentationf

LIMITLESS POTENTIAL | LIMITLESS OPPORTUNITIES | LIMITLESS IMPACT

SMALL POWER LOAD DISAGGREGATION IN OFFICE BUILDINGSBASED ON ELECTRICAL

SIGNATURE CLASSIFICATION

IEEE Energycom 2016 conference

Ana [email protected]


The goalUnderstand end

use energy consumption at

system level

The perfect Energy AuditAccurate, cheap and low intrusive infrastructure

The starting point

Building survey and energy

data collection


Non Intrusive Appliance Monitoring (NIALM)

Monitoring the electrical signal in a single point of the network, detect changes in current and voltage for recognizing what appliances are being used in the house and their individual energy consumption.


IDEA Disaggregate only small powers

• Significate source of energy end-use• Major source uncertainty ion energy

assessment

But is not so easy…


Let me introduce you to NIALMNIALM were first developed by George Hart in the 80s

Electrical Signatures

Transients Steady-state

Machine learning Methods

Supervised Unsupervised

Classification Clustering Regression

Theoretical framework:


My NIALM Model


IMPORT DATA: CURRENT AND VOLTAGE AT 1 MILLISECOND

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

x 104

-1

-0.5

0

0.5

1

1.5

Time (ms)

Curre

nt (A

)


DATA PROCESSING

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x 104

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Time (ms)

Curre

nt R

MS

(A)


EVENT DETECTION

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

x 104

0

0.05

0.1

0.15

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0.3

0.35

0.4

Time (ms)

Irms

(A)


ELECTRICAL SIGNATUREAPPLIANCE CHARACTERISTIC ELECTRICAL FEATURES

Transient Steady-StateRMS


CLASSIFICATIONDECISION TREE USING KNIME


Experiments


Results


Future workImplement NIALM methods in Office buildings


NIALM might be the key…

Thanks for your attention

Any question…?

IEEE presentationf

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