Using Wavelet Transform to Disaggregate Electrical Power Consumption into the Major End-Uses

Francisco J. Ferrández-Pastor Juan M. García-Chamizo

Mario Nieto-Hidalgo Vicente Romacho-Agud

Francisco Flórez-Revuelta

Department of Computing Technology [email protected]

Main electric panel


Main electric panel I t( ) = I1(t)+ I2 (t)+ I3(t)+ I4 (t)+..+ Im t( )

Im (t) = Im1(t)+...+ Imn (t)

I4 (t) = I41(t)+...+ I4n (t)

I3(t) = I31(t)+...+ I3n (t)I2 (t) = I21(t)+...+ I2n (t)

I1(t) = I11(t)+...+ I1n (t)


main electric panel

current transformer

CT

data acquisition

da wavelet transform

WT

Ida t( ) =I(t)CT

I t( ) = I1(t)+ I2 (t)+ I3(t)+ I4 (t)+..+ Im t( )

Im (t) = Im1(t)+...+ Imn (t)

I4 (t) = I41(t)+...+ I4n (t)

I3(t) = I31(t)+...+ I3n (t)I2 (t) = I21(t)+...+ I2n (t)

I1(t) = I11(t)+...+ I1n (t)


data acquisition

da

current transformer

CT

wavelet transform WT

Ida t( ) =I(t)CT

I t( ) = I1(t)+ I2 (t)+ I3(t)+ I4 (t)+..+ Im t( )


Supervised phase The events that produce electrical connection and dis-connection of appliances (lighting, microwave, television, etc.) are classified as adapted wavelets. When profiling the appliances to build the knowledge base, we make controlled connections and disconnections that generate specific signatures for the various power consumption modes for each appliance or device. Monitoring phase The aggregate curve of electrical consumption (captured during a monitoring process) is processed applying a wavelet transform using adapted wavelet functions Ψi. Actual and recorded data are used to identify power events (connection/disconnection of appliances).


Wavelet forms acquisition: different adapted wavelets are obtained

0 20 40 60 80 100 120 140−1.5

−1

−0.5

0

0.5

1

1.5

2

time (sec)

Washing machine

0 2 4 6 8 10 12 14 16 18 20−1

−0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

time (sec)

Refrigerator

0 100 200 300 400 500 600−1

−0.5

0

0.5

1

1.5

time (sec)

Electric hob

0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1

1.2

1.4

time (sec)

Plasma TV

0 5 10 15 20 25 301.3

1.4

1.5

1.6

1.7

1.8

1.9

2

time (sec)

Incandescent light

0 5 10 15 20 25 30 35 40 450

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

time (sec)

Led TV

Washing machine Fridge

Electric oven Plasma TV

Microwave Air conditioned Department of Computing Technology [email protected]

400 600 800 1000 1200 1400 1600 1800

2

4

6

8

10

12

14

16

18

Time (sec)

Elec

trica

l cur

rent

(Am

per)

Sampling time: 1 second

b2b3b1

adapted wavelet location on the signal

Example of a wavelet Ψa,b

(t) of fixed dilation at three different locations on the signal. A large positive value of coefficients is returned in location b2.

Ψa,b

(t)

We calculate coefficients of WT with each adapted wavelet

0 20 40 60 80 100 120 140−1.5

−1

−0.5

0

0.5

1

1.5

2

time (sec)

Washing machine


Analyzed Signal

Percentage of energy for refrigerator adapted wavelet

Time

Scal

es a

0 0.5 1 1.5 2

x 104

1

2

3

4

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

Adapted wavelet

Analyzed Signal

Percentage of energy for washing machine adapted wavelet

Time

Scale

s a

0 0.5 1 1.5 2 2.5

x 104

1

2

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Adapted wavelet

Example of adapted wavelet transform with pattern functions Ψi and analysis of energy Eψi (Scalograme)


}  This method has been tested in a real environment. ◦  Using an energy meter in a house ◦  During 7 days ◦  Sampling time 1Hz

}  A set of seven signal forms (f1 to f7) has been taken. ◦  For each form an adapted wavelet (ψ1 to ψ7) is built.

}  Wavelet Transform for each adapted wavelet ψ1 to ψ7 is calculated when an event is detected. ◦  A vector of energy coefficients: [wcf1, wcf2, wcf3, wcf4,

wcf5, wcf6, wcf7] is obtained. ◦  The argmax {wcfi } provides the detected form fi.



Accuracy of 92.2%

}  Wavelet transform with adapted signals is a technique with great potential for power consumption analysis

}  This work shows that data captured by power meters, in a non-intrusive way, can be treated with wavelet analysis to identify activities and to disaggregate the total electricity into the major end-uses

}  This method could be able to recognise behaviour of people and may be used to develop new services and in energy management


Francisco J. Ferrández-Pastor Juan M. García-Chamizo

Mario Nieto-Hidalgo Vicente Romacho-Agud

Francisco Flórez-Revuelta


Using Wavelet Transform to Disaggregate Electrical Power Consumption into the Major End-Uses

Technology

Transcript of Using Wavelet Transform to Disaggregate Electrical Power Consumption into the Major End-Uses