Algorithm Evaluation

In order to test the accuracy of the proposed algorithm, the whole system including both

software and hardware is set up in the laboratory environment. For evaluation purpose, a set

of appliance is selected to be monitored under the established system. The selected appliances

are listed as Table7-1.

Table 7-1 List of appliances used for evaluation

Appliances Mode Power [W] Type

Heater Mode 1 657.1 Resistive

Mode 2 971.1 Resistive

Fan

Mode 1 30.1 Inductive

Mode 2 32.7 Inductive

Mode 3 42.7 Inductive

ES light bulb None 17.9 Non-linear

Fridge Lighting 16.4 Resistive

Operation 74.2 Inductive

Microwave Lighting 24.7 Resistive

Operation 1330.5 Non-linear

Monitor None 25.7 Non-linear

As it shown in Table 7-1, 6 appliances with 11 working modes in total are included in the test

environment. The selected appliances not only cover all typical types of loads, namely

resistive, inductive and non-linear, but also drawn a wide range of power from 17.9W to

1330.5 W. It can be also noticed that some appliance consumes similar power, which could

be problematic for traditional power value-based load monitoring algorithm.

The experiment is conducted during the day timing and all the appliances are expected to be

switched on and operate at least once. As mentioned previously, the raw voltage and current

measurement are sampled at 10 kHz and the other values, including RMS values, power

values and load signatures are then calculated based on the measurement and recorded at 1

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operation. Finally, the event log is analysed and compared with the power plot to investigate

the accuracy of algorithm.

One example of the event log created by the software is shown as Figure 7.7:

Figure 7.7 Event logs created by the software

As it shown in Figure 7.7, the data, time, identified event and appliances are recorded in

sequence. Based on this record, the events on 10/12/2014 can be labelled on the power plot of

the same day, which is shown as Figure 7.8. Although Figure 7.7 only displays the events in

the morning, the power plot of both the morning and the afternoon are illustrated with the

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Figure 7.8 Power Plot with labelled events recorded by algorithm

Microwave on & off Heater mode1 on & off Heater mode2 on & off

Fridge on & off

Fridge on Fridge off Fridge on Monitor on ES bulb on Fridge off Fridge off Fridge on Fan on & off Fridge on Fridge off Monitor off ES bulb off Fridge on Fridge (lamp) on & off

Microwave (lamp) on & off

Undetected event(s) View 1 View 2 View 3 View 4 View 5 View 6

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Table 7-2 Experimental Results of Individual Appliances

Appliances Mode Total

Events Detected Events Missed or Fault Events Accuracy Heater Mode 1 6 6 0 100% Mode 2 2 2 0 100% Fan Mode 1 4 2 1 50%

ES light bulb None 2 2 0 100%

Fridge Lighting 14 13 1 92.8%

Operation 12 12 0 100%

Microwave Lighting 12 12 0 100%

Operation 6 6 0 100%

Monitor None 2 1 1 50%

As Figure 7.8 shows, the power plot starts from 11 am to 6 pm. To start with, the three events

with high peak and very short time length which occurs around 1 pm can be noticed as events

relating to microwave. It indicates that it is typically used around lunch time and the usage

time of it is normally 1 min. Apart from that, the lamp in microwave is also associated with

the operation of microwave. It is normally switched on and off for people to get food in and

out shortly before and after the microwave operation. A typical of microwave cycle is shown

in View 3 of Figure 7.8. It starts with an extremely short peak around 25 W (marked as blue

circle), which represents the lamp switched on and off, then a one-minute operation of

microwave is followed, finally another short peak indicates that the lamp is open and food is

get out of the microwave.

Although it is also fitted with a lamp, the fridge doesn’t work along with its lamp regularly as microwave because the compressing cycle of the fridge is independent with the lighting of

the fridge. It can be noticed that the lamp of the fridge (small peak, marked as red triangle) is

switched on and off randomly throughout the whole day while the compressing process starts

periodically and lasts the same time length for each operation. View 2 is an interesting

demonstration of activities relevant to both fridge and microwave. It can be noticed that the

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can be inferred that some food is taken out from fridge and put into the microwave. Both

lamps of fridge and microwave are switched off shortly and then the microwave starts to heat

the food. The results generated by the algorithm match the activities exactly in this scenario.

The monitor and ES light bulb are turned on around 2 pm in sequence and then turned off at

the end of test around 6 pm. During this time period, the heater and fan are also switched on

and operation from a while. In terms of the heater, there is a thermal protection mechanism to

prevent from overheating, therefore it can be found that the operation of cannot last for long

time, and a gap can be observed between two operations.

The 4 undetected events are marked as black rectangle in Figure 7.8 and they all consume

power less than 30W. On the contrary, the appliances with large current draw can be

identified perfectly. In real test, every appliance doesn’t draw exactly same current all the

time and the current variation range is different appliance from appliance. When multiple

appliances in operation, the variation range becomes more unpredictable and the noise

current may either cancel out or build up. When an event detected in this situation, the event

signature is actually the combination of load signature and noise. It is possible that the event

signature with this noise added exceeds the tolerance of the corresponding appliance, which

fails the recognition. Compared to appliance with large current rating, the appliance drawing

small current is much more likely to be affected by this noise build up by multiple appliances

operation.

Above all, the evaluation of this algorithm is still very promising. 56 out of 60 events are

successfully detected, which results in a 93.3% rate in this scenario. The same test also runs

for another two days and it gives a 92.9% overall rate (92 out of 99). Considering the high

accuracy and long running time, it can be concluded that the algorithm is able to monitor the

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