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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
Len
−
Len
1
∑
Value
=
=
i
0
Sub
Len
−
Cnt
∑
=
=
i
0
Partial
⎡
−
n
∑
⎢
⎢
+
=
i
Partial
⎢
⎢
⎣
⎡
−
n
2
∑
Sub
⎢
i
Sub
⎢
+
=
+
i
0
−
⎢
n
1
⎢
⎣
e
Electro Industries/GaugeTech
Sub 1
...
Len
i
1
Value
i
Cnt
⎤
2
Value
⎥
⎡
⎡
⎡
i
Len
⎥
×
−
0
⎢
1
⎢
⎢
⎥
⎣
⎣
n
⎣
⎥
⎦
⎤
⎥
−
⎡
⎡
Sub
Len
⎥
−
×
0
n
1
⎢
⎢
−
⎣
⎥
⎣
2
x
(
n
) 1
⎥
⎦
Doc # E107706 V1.25
Sub 0
Len
⎤
⎤
−
⎤
Cnt
×
⎥
Pct
⎥
⎥
⎦
⎦
Len
⎦
−
⎤
⎤
Cnt
×
Pct
⎥
⎥
⎦
⎦
Len
Partial
Predict
Cnt
Len
2-11
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