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Electro Industries/GaugeTech
Doc # E107706 V1.25 2-11
Example: Using the previous settings of 3 five-minute intervals and a new setting of 120%
prediction factor, the working of the Predictive Window Demand could be described as follows:
At 12:10, we have the average of the subintervals from 11:55-12:00, 12:00-12:05 and 12:05-12:10.
In five minutes (12:15), we will have an average of the subintervals 12:00-12:05 and 12:05-12:10
(which we know) and 12:10-12:15 (which we do not yet know). As a guess , we will use the last
subinterval (12:05-12:10) as an approximation for the next subinterval (12:10-12:15). As a further
refinement, we will assume that the next subinterval might have a higher average (120%) than the
last subinterval. As we progress into the subinterval, (for example, up to 12:11), the Predictive
Window Demand will be the average of the first two subintervals (12:00-12:05, 12:05-12:10), the
actual values of the current subinterval (12:10-12:11) and the predistion for the remainder of the
subinterval, 4/5 of the 120% of the 12:05-12:10 subinterval.
# of Subintervals = n
Subinterval Length = Len
Partial Subinterval Length = Cnt
Prediction Factor = Pct
Sub
n
...
Sub
1
Sub
0
Partial Predict
Len Len Len Cnt Len
Len
Value
Sub
Len
i
i
∑
−
=
=
1
0
Cnt
Value
Partial
Cnt
i
i
∑
−
=
=
1
0
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−
−×
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⎥
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⎢
⎢
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⎢
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+
∑
−
=
Pct
Len
CntLen
n
Value
Partial
n
i
i
1
2
0
⎥
⎦
⎤
⎢
⎣
⎡
×
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−
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−
−
+
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+
−
−
=
∑
Pct
Len
CntLen
nx
SubSub
n
Sub
n
n
i
i
)1(21
10
2
0