Electro-Voice 1252, 250 operation manual Partial = ∑Value

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−1

Sub =

∑Valuei

i=0

Len

Cnt −1

Partial =

∑Valuei

i=0

Cnt

⎡

∑Value ⎤

n−2

⎢

i

=

0

i ⎥

⎡

⎡⎡ Len − Cnt ⎤

⎤

⎤

⎢Partial +

⎥

⋅

⎢1

− ⎢⎢

⎥

⋅ Pct

⎥

⎥

n

Len

⎢

⎥

⎣

⎣⎣

⎦

⎦

⎦

⎢

⎥

⎣

⎦

⎡ n−2

⎤

⎢

∑ Sub

Sub

− Sub

⎥

⎡⎡ Len − Cnt ⎤

=

0

i

−

⎤

+ ⎢

i

+

0

n 1

⎥

⋅ ⎢⎢

⎥

⋅ Pct

⎥

n −1

Len

⎢

2x(n −1)

⎥

⎣⎣

⎦

⎦

⎢

⎥

⎣

⎦

e Electro Industries/GaugeTech Doc # E107706 V1.25 2-11