Using wavelet transform to disaggregate electrical ... - Semantic Scholar

Using wavelet transform to disaggregate electrical power consumption into the major end-uses Francisco Javier Ferr´ andez-Pastor1 , Juan Manuel Garc´ıa-Chamizo, Vicente Romacho-Agud, and Francisco Fl´orez-Revuelta2 1

2

Dept. of Computing Technology,University of Alicante, P.O. Box 90, E-03080, Alicante, Spain Faculty of Science, Engineering and Computing, Kingston University, Kingston upon Thames, United Kingdom

Abstract. This paper proposes an innovative method based on wavelet transform (WT) to decompose the global power consumption in elemental loads corresponding to each appliance. The aim is to identify the main entities that are responsible of total electricity consumption. The research demonstrates that the WT could be used to identify simpler electrical consumption patterns as a part of total consumption curve. Real power measurements has been used in this work. The results obtained have shown the accuracy to decompose consumption curves using WT. This work will be used to develop new energy management services that will improve ambient intelligence. Keywords: Disaggregated energy, wavelet transform, electrical consumption, NILM.

1

Introduction

Advances on monitoring and controlling of energy consumption improve development of energy management systems and smart grids to be used at home, industry and any complex installation. Know how each appliance contributes to total consumption is used to optimize scheduling, avoid waste and automatize some tasks. In the reverse way, the power consumption curve can be decomposed in its constituents or individual appliances or entities that are working simultaneously. Some techniques as wavelet-transform, statistical analysis and other ones could be used to that end. Figure 1 shows the consumption of each major appliance (different color) riding over the other ones to conform the global consumption curve I(t). This could be expressed as: I(t) = i1 (t) + i2 (t) + ... + in (t)

(1)

being I, the electrical current of the appliance j, and n the number of appliances. WT has been used to analyze electrical signals [1]. It works obtaining series of non-stationary functions [21]. In this paper, adapted wavelets are used to

Fig. 1. Aggregated electrical consumption.

descompose power consumtion. Related works and concepts about WT analysis are revised, followed by a proposal to obtain consumption patterns. Finally, the experiments made at housing, that shown the capabilities of this method to identify consumption pattern of the appliances.

2

State of knowledge about analysis of power consumption

There are two main approaches to decompose total electricity consumption: Intrusive Load Monitoring (ILM) and Non-Intrusive Load Monitoring (NILM). Traditional load monitoring techniques are considered intrusive because the consumption of each appliance is measured separately [5,11]. The NILM concept, proposed in [9], consist of analyzing the global power consumption to infer the appliances being used. It is the commonly used method today. A review on NILM approaches is presented in [10], that point out NILM could provide meaningful feedback when used in available commercial devices. An evaluation of the realistic potential and limitations of NILM techniques is presented in [17]. Recent works have developed new non-intrusive techniques for load monitoring. [20] provides a comprehensive overview of NILM systems and methods to decompose the measured power consumption. Wavelet analysis has been widely applied in signal analysis, image processing, numerical analysis, etc. [8]. Its capabilities to study the signal taking into account time and frequency simultaneously has been used to extract information by mean of convolution with an adapted wavelet. A new method to process radar signal by using WT techniques to recognize patterns is proposed in [12]. The experimental results demonstrate that it is effective and radar signal could be processed in real time. WT has been used to obtain characteristics in medical electrocardiographic signal [6]. Applications of WT in electrical systems are analysis of electrical power [4], power quality information [2], fault detection and location [13] and signal processing [7]. A NILM proposal using WT is the analysis of transient features on the power signatures [3]. Recent research show the usefulness of multi-modal approach using data from separate environmental sensors (light, sound, etc.) [1,15].

Sampling time: 1 second 18 adapted wavelet location on the signal

Electrical current (Amper)

16 14 12

b2

b1

b3

10 8 6 4 2 400

600

800

1000

1200

1400

1600

1800

Time (sec)

Fig. 2. Pattern detection with adapted wavelet. A wavelet Ψa,b (t) of fixed dilation at three distinct locations on the signal. A large positive value of coefficients is returned in location b2.

3

Electrical signal characterization using wavelet

The Wavelet Transform WΨ S of a signal S(t) is defined as the sum along time of the signal multiplied by scaled and shifted versions of the wavelet function Ψ . It is given by: +∞ Z WΨ S(a, b) = S(t)Ψa,b dt (2) −∞

1 Ψa,b (t) = √ Ψ ∗ a



t−b a

 (3)

where * denotes complex conjugation, Ψa,b (t) is a window function called
the mother wavelet, a is a scale factor and b is a translation. Here Ψ t−b is a a shifted and scaled version of a mother wavelet, which is used as a base for wavelet decomposition of the input signal. Electric current is captured in realtime by an sensor and used as electrical variable (figure 2). Acquisition of the current signal is simple and its value integrates the behaviour of each appliance. The process of capturing every signature is a monitored procedure: the variations of the electrical current (rms) are measured when a device is connected, when it is in a stable state and when it is disconnected. In this paper, a Pattern Detection with Adapted Wavelets approach is used. Patterns and their values in the scale-position (frequency-time) can then be identified in electrical power signals. Figure 2 shows an example where the adapted wavelet (pattern of a signal) is fixed at different locations to detect if it exists in the captured signal. Diferents energy coefficients are obtained when the wavelet transform with an specific pattern is applied to the electrical signal in different locations. Adapted signals can be created as new wavelet patterns [19,14,16]. In order to build a new wavelet pattern, a signal form fi (appliance signal obtained in supervised phase) is necessary. Each new wavelet Ψi is obtained by approximating

light and microwave forms

whasing machine and electric hob forms

9

20

8

18

microwave ON

electrical current (Amp)

electric current (Amp)

light ON

light ON

light ON

5

4

electric hob ON

whasing machine cycle

electric hob OFF

16

7

6

whasing machine ON

light OFF

light OFF

3

14 12 10 8 6

2 4 1 2 0 0

500

1000

1500

(a)

2000

2500

0 0

500

1000

1500

2000

2500

(b)

Fig. 3. Process of forms detection in supervised phase (a) Lights switched on-off and microwave (b) Electric oven and washing machine.

each form fi using conversion functions. The usefulness of this construction is demonstrated when it is applied to a detection problem [12,18]. This task consists on employing the adapted wavelet to identify the patterns stemming from the basic form on an aggregate signal.

4

System Details

The proposed approach consists of two phases, supervised and monitoring phase. Supervised phase The events that produce electrical connection and disconnection of appliances (lighting, microwave, television, etc.) are classified as adapted wavelets. When profiling the appliances to build the knowledge base, the users make controlled connections and disconnections what generates specific signatures for the various power consumption modes for each appliance or device. The system records the moments in which the appliance is switched on/off and the stable state after the connection (figure 3). This work considers some of the most usual devices at home: fridge f1 , microwave f2 , washing machine f3 , electric oven f4 , plasma TV f5 , lights f6 , air conditioner f7 (Figure 4). First, following a supervised human-guided process, the signals associated with the activation of each of the main appliances are recorded. Then, a wavelet representing each of the signals Ψi is obtained. Finally, a wavelet pattern of each form is made. The main feature used are the Energy EΨi obtained with the coefficients of the wavelet transform. Ψi is an adapted wavelet function for each pattern fi .The wavelet transform is computed on the original signal using adapted wavelet functions Ψi . Monitoring phase The aggregate curve of electrical consumption (captured with a monitoring process) is processed applying a wavelet transform using adapted wavelet functions Ψi . Real time data and data recorded are used to identify power events (connection/disconnection of appliances). Once an event

Incandescent light

Refrigerator 4

Washing machine

2

2

1.9

1.5

1.8

1

Electric hob 1.5

3.5

3

1

2.5

2

0.5 1.7

0.5

1.5

1.6

0

1.5

−0.5

1.4

−1

1

0

0.5

0

−0.5

−0.5

−1

0

2

4

6

8

10 time (sec)

12

14

16

18

1.3

20

0

5

10

(a)

15 time (sec)

20

25

−1.5

30

0

20

40

60

80

100

120

(b)

0

100

200

300 time (sec)

(c)

400

500

600

(d)

Fluorescent light

Plasma TV 1.4

−1

140

time (sec)

Led TV

1.8

0.5

0.45 1.7 1.2

0.4

1.6 1 0.35

1.5 0.3 0.8

1.4

0.25

0.6 0.2 1.3

0.15 0.4 1.2

0.1

0.2 1.1 0.05

0

0

10

20

30

40

50

60

70

1

0

5

time (sec)

10

15

20

25

0

0

5

10

15

20

time (sec)

(e)

25

30

35

40

45

time (sec)

(f)

(g)

Fig. 4. Adapted wavelets ψi captured in the supervised phase (a) fridge (b) Microwave (c) Washing machine (d) Electric oven (e) Plasma TV (f) Light (g) Air conditioned.

is detected a frame of the power signal captured is analysed using wavelet transform treatment and pattern recognition techniques. The frame is treated with pattern-adapted wavelets Ψi , recorded in supervised phase. WT are sensitive to the choice of the initial point from which the signal is analysed. To overcome this problem and to determine the initial point (IP ) the instant tk when the event begins is established in Algorithm 1; where Itk is the electrical current at t = tk . Given a set of adapted waveltes Ψi the goal is to obtain energy EΨi of each of them. Figure 5 shows a scalogram once the wavelet transform with adapted wavelet for fridge appliance EΨf ridge is performed. The coefficients obtained from the wavelet analysis provide a close approximation to the original signal and therefore may be used to identify this appliance. Figure 6 explains the monitoring phase. Analyzed Signal Adapted wavelet

Percentage of energy for refrigerator adapted wavelet 4 2.4 2.2 2 1.8

Scales a

3

1.6 1.4 1.2 2

1 0.8 0.6 0.4

1 0

0.5

1

1.5

Time

2 4

x 10

Fig. 5. Example of Energy EΨ obtained by WT between electrical signal and fridge adapted wavelet.

Use case. This method has been tested in a real environment. An energy meter was installed in a house where the aggregate and individual consumption

Fig. 6. Monitoring phase: WT between frame captured and each wavelet adapted form.

of different devices have been captured in a supervised phase during 7 days ( sampling times: 1Hz and size of the frame: 8 samples/points). Appliances identified are associated with a series of devices that connect when the event occurs. A set of seven signal forms (f1..7 ) has been taken (Figure 4). This signal forms corresponding to the appliances captured in the supervised phase, for each form an adapted wavelet (ψi..7 ) is built. Wavelet Transform for each adapted wavelet ψi..7 is calculated when an event is detected. A vector of energy coeficients: [wcf 1 , wcf 2 , wcf 3 , wcf 4 , wcf 5 , wcf 6 , wcf 7 ] is obtained. The arg max {wcfi } provides the form fi detected. A local absolute maximum in wavelet transform coefficients of EΨi indicates the type of appliance. Figure 7 shows the confusion matrix of 7 days tested in the house.

Algorithm 1 Calculation of the initial point (IP) of an event. if |∆Ik | = |Itk −

1 n

n P

Itk−j | > threshold and sign(∆Ik ) = sign(∆Ik+1 ) then

j=1

IP = Itk if sign(∆Ik ) > 0 then appliance is ON else appliance is OFF

5

Conclusions and future works

Wavelet transform with adapted signals is a technique with great potential for power consumption analysis systems. However, it has not been exploited yet. This work shows that data captured by power meters, in a non-intrusive way, can

Fig. 7. Case of use: Confusion matrix obtained.

be treated with wavelet analysis to identify activity electrical and to disaggregate the total electricity into the major end-uses. This method is able to recognise behaviours of people and may be used as a support for the development of new services and in energy management services and smart grids. Can be used in industrial or domestic scenarios in a simple way with low cost systems. In the near future, additional datasets will be collected to test the accuracy of results under different combinations of use of appliances. .Future works will analyze unsupervised systems with WT using different classifiers (e.g. artificial neural networks, genetic or statistical methods) and other kind of electrical data (e.g. transient signal, active and reactive power or power factor)

References 1. Berges, M., Soibelman, L.and Matthews, H.: Leveraging data from environmental sensors to enhace electrical load disaggregation algorithms. In: Tizani, W. (ed.) Proceedings of the International Conference on Computing in Civil and building Engineering (2012) 2. Brito, N., Souza, B., Pires, F.: Daubechies wavelets in quality of electrical power. In: Harmonics and Quality of Power Proceedings, 1998. Proceedings. 8th International Conference On. vol. 1, pp. 511–515 vol.1 (1998) 3. Chang, H.: Non-intrusive demand monitoring and load identification for energy management systems based on transient non-intrusive demand monitoring and load identification for energy management systems based on transient non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses. Energies 5, 4569–4589 (2012)

4. Chul-Hwan, K., Aggarwal, R.: Wavelet transforms in power systems. i. general introduction to the wavelet transforms. Power Engineering Journal 14(2), 81–87 (2000) 5. Farinaccio, L., Zmeureanu, R.: Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy and Buildings 30(3), 245 – 259 (1999) 6. Figueiredo, M., Almeida, A., Ribeiro, B.: Home electrical signal disaggregation for non-intrusive load monitoring (nilm) systems. Neurocomputing 96(0), 66 – 73 (2012) 7. Haibo, H., Shijie, C., Youbing, Z., Nguimbis, J.: Home network power-line communication signal processing based on wavelet packet analysis. Power Delivery, IEEE Transactions on 20(3), 1879–1885 (2005) 8. Hariharan, G., Kannan, K.: Review of wavelet methods for the solution of reaction diffusion problems in science and engineering. Applied Mathematical Modelling 38(3), 799 – 813 (2014) 9. Hart, G.: Non-intrusive appliance load monitoring. Proceedings of the IEEE 80(12), 1870–1891 (1992) 10. Jiang, L., Jiaming, L., Suhuai, L., Jin, J., West, S.: Literature review of power disaggregation. In: Modelling, Identification and Control (ICMIC), Proceedings of 2011 International Conference on. pp. 38–42 (2011) 11. Kolter, J.Z., Johnson, M.J.: Redd: A public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA (2011) 12. Li, C., Zhang, C.: The pattern recognition of radar signal by self-adapted wavelet. In: Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2007 International Symposium on. pp. 1199–1203 (2007) 13. Liang, J., Elangovan, S., Devotta, J.: A wavelet multiresolution analysis approach to fault detection and classification in transmission lines. International Journal of Electrical Power and Energy Systems 20(5), 327 – 332 (1998) 14. Mesa, H.: Adapted Wavelets for Pattern Detection, vol. 3773, pp. 933–944. Springer Berlin Heidelberg (2005) 15. Parson, O., Ghosh, S., Weal, M., Rogers, A.: Using hidden markov models for iterative non-intrusive appliance monitoring. In: Neural Information Processing Systems workshop on Machine Learning for Sustainability (December 2011), event Dates: 17 December 2011 16. Ravier, P., Amblard, P.: Combining an adapted wavelet analysis with 4th-order statistics for transient detection. Signal Processing 70(2), 115–128 (October 1998) 17. Sharp, T.: Non-intrusive load monitoring systems: Considerations for use and potential applications. ACEE Summer Study on Energy Efficiency in Buildings 2, 241–247 (1994) 18. Shen, D., Ip, H.: Discriminative wavelet shape descriptors for recognition of 2-d patterns. Pattern Recognition 32(2), 151–165 (February 1999) 19. Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Elsevier (2002) 20. Zoha, A., Gluhak, A., Imran, M.A., Rajasegarar, S.: Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors 12(12), 16838– 16866 (2012)