Electro-Voice 1252 - Manual (Page 35)

e 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
Subn ... Sub1 Sub0 Partial Predict
Len Len Len Cnt Len
Len
Value
Sub
Len
i
i
∑
−
= =
1
0
Cnt
Value
Partial
Cnt
i
i
∑
−
= =
1
0
⎥
⎦
⎤
⎢
⎣
⎡
⎥
⎦
⎤
⎢
⎣
⎡
Ч
⎥⎦
⎤
⎢⎣
⎡ −
− Ч
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎣
⎡
+
∑
−
=
Pct
Len
Cnt Len
n
Value
Partial
n
i
i
1
2
0
⎥
⎦
⎤
⎢
⎣
⎡
Ч
⎥⎦
⎤
⎢⎣
⎡ −
Ч
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎣
⎡
−
−
+
−
+ −
−
=
∑
Pct
Len
Cnt Len
n x
Sub Sub
n
Sub
n
n
i
i
) 1 ( 2 1
1 0
2
0