1. 61 Events Total Test Home #1: Controlled Test DryerGarage Toaster Oven 1Oven 2

2. Appliance Table Generation Appliance 1 = Oven 1 or Dryer – Power difference only 10 Watts Appliance 2 = Second Oven Appliance 3 = garage door opener Appliance 4 = state for noisy lights Missing toaster oven! – Why? 4, 0, 0, 5615, 122 4, 1, 0, 2311, 136 4, 2, 0, 393, 85 4, 3, 0, 872, 142

3. Results in Test Home 3 Real Power Load Data in Test Home’s Kitchen (Dec 2nd) Correct: Dryer Correct: Toast Oven Incorrect: Dryer Correct: Garage Door Opener Incorrect: Dryer + 2 nd Oven Correct: Dryer +Toast Oven Correct: Dryer Correct: Toast Oven Incorrect: Garage Door Opener

4. Real Testing Ran with previously defined state table on 3 days of data Looking for the oven signature – Found on 12-12-09 – Not found on 12-10-09 or 12-11-09 Sample output: 12/11/2009 7:39:30 1st_Oven On 12/11/2009 7:39:51 1st_Oven Off Time (s) Real Power (W)

5. Test House #2 from 11-3-09 Test house #2 had two main AC units Goal: Find these units AC #1 AC #2 Time AC #1AC #2 Compressor Air Handler Compressor Air Handler

6. Clustering from 11-3-09 Reactive Power (VAr) AC #1 Off AC #2 Off AC #1 On AC #2 On

7. Generate State Tables State generating algorithm correctly identified both AC units – Impressive since it only takes real power into account – Also realized that the first air conditioner was a two state appliance – Missed the second state on AC #2 Generated State Table (Other Results Omitted) 7, 4, 0, 2400, 50 7, 5, 0, 3650, 150 7, 5, 1, 851, 104

8. Meter Monitoring We surmise that one day of learning is not sufficient Based on Data we would with assume the AC ran on 11/7/2009 and 11/26/2009

9. Bandwidth With Above Threshold had less than 1000 events per day That is less than 1% of original data Or assuming 24B per reading under 15 kB per day

10. Bandwidth cont. With Above Threshold had less than 2000 events per day That is less than 2.5% of the original data Or assuming 28B per reading under 60 kB per day

11. Appliance Tables Generated Appliance Tables for three days from November and December Same appliances were identified over the three days Appliance tables are unique

12. Conclusions Smoothing Algorithm Cut down the data to under 2% in our testing – This should easily be transmittable to the head end for processing The learning phase produced distinguishably different state tables in different environments furthermore, similar appliances were found over separate learning periods in the same environment The meter monitoring algorithm worked well if: – It had a completely accurate state table – All appliances had distinguishably different loads

13. Further Research Are there more ways to classify appliances other that through real/reactive power? – Maybe use the frequency of the event – Maybe use the time of day What improvements can be made to the real power only NILM? – Must be improved for use. How long does the meter need to be in learning mode to pick up all appliances? – We suspect this is dependant on the habits of the residents.

14. Questions Feel free to ask away…..