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. 2019 Nov 28;19(23):5236. doi: 10.3390/s19235236

Algorithm 3 Event classification penalty (ECPenalty).

Input:

G_{T} = {g_{1}, g_{2} \dots g_{t}}

is the ground truth of appliance m

P_{T} = {p_{1}, p_{2} \dots p_{t}}

is the predicted values of appliance m

C_{S t a t e}

is the clustered operational state data of appliance m
Output:

\sum_{i = 1}^{T} E C_{i}^{(m)}

is the total Event Classification Penalty for appliance m

1:
Set $T o t a l P e n a l t y_{m} = 0$
2:
for $t = 1$ toTdo
3:
Get datapoint $g_{t}$ and $p_{t}$
4:
if $g_{t} > 0$ and $p_{t} > 0$ then
5:
Compute closestCluster $(g_{t}, C_{S t a t e} [C_{i_C_{m e a n}}])$
6:
Set state of $g_{t}$ to $C_{i_S_{i}}$
7:
Compute closestCluster $(p_{t}, C_{S t a t e} [C_{i_C_{m e a n}}])$
8:
Set state of $p_{t}$ to $C_{i_S_{i}}$
9:
end if
10:
if state of $g_{t} \neq p_{t}$ then
11:
Set penalty $E C_{t}^{(m)}$ equal to 1
12:
else
13:
Set penalty $E C_{t}^{(m)}$ equal to 0
14:
end if
15:
end for
16:
return $\sum_{i = 1}^{T} E C_{i}^{(m)}$