Eliminating steady-state detected transitions using bruteforce thresholding

I now have two sets of information for when the fridge cycles occur, created using two different algorithms:

1. A list of possible 'on' and 'off' events, selected through steady-state analysis of the house power data
2. A list of every possible intervals in which the fridge could be on, selected through brute-force thresholding of the house power data

The steady-state analysis identified an unreliable list of 'on' and 'off' events. Some transitions correctly identified a state transition of the fridge, while others identified state transitions of other appliances. In addition, the steady-state analysis failed to detect some state transitions of the fridge due to noise generated from other appliances.

My aim for today was therefore to remove state transitions caused by other appliances, leaving only those generated by the fridge. I did this by eliminating state transitions using a combination of:

• the possible intervals of operation collected through brute-force thresholding
• the fridge's known signature.

I generated 3 rules which I used to remove incorrect state transitions:

1. No state transition belongs to the fridge if it occurs outside of the intervals of possible operation
2. No 'on' transition belongs to the fridge if it occurs less than the minimum 'on' duration after an impossible interval of operation
3. No 'off' transition belongs to the fridge if it occurs less than the minimum 'on' duration prior to an impossible interval of operation

This filtered the list of state transitions, leaving the list of plausible transitions shown below.

• The black line represents the household's power level
• The blue line represents the fridge's power level
• The green negative peaks represent detected 'on' transitions
• The red negative peaks represent detected 'off' transitions

This is a fairly good start. Nearly all cycles during the night were correctly identified, while most cycles during the day had at least either their 'on' or 'off' transition detected. However, there are still a number of erroneous state transitions which must be removed.

The remaining steps to get the best estimate of each fridge cycle are:

1. Identify cycles for which we have a high confidence in their correctness
2. Fill in gaps between correctly classified cycles using a combination of:

• Low confidence state-transitions
• Possible intervals for fridge cycles
• Applying the model of typical fridge behaviour using surrounding high confidence cycles