adrian116 schrieb:
> Dear all, 
> 
>        I am doing a final year project to estimate the power consumption
> of a chiller plant. I have built a basic model by using Kalman Filter and
> get a result with some percent of error.
> 
> For my model, I used a moving windows say 60 sets of data, then calculated
> the parameters to be estimated from these data sets. Use these a,b,c,d,e
> and the measurement of 61set of data to estimation the power at t=61. So
> a,b,c,d,e calculated from 2-61sets of data sets and measurement of 62 set
> of data are used to determine the power at t=62 and so on.
> 
> For this case, I need to handle all 60 sets in the moving windows each
> time. It seems strange and time consumption.
> 
> My teacher asked me try to introduce forgetting factor to my model. But i
> have searched many material in the web and didn't find any help. Do you
> guys have any idea?
> 
> Thank You.
> 
> 

I do not know what your really trying to approach. As far as I know the 
Kalman Filter is supposed to predict and filter at each measurement 
step. If you are using averaging windows you induce correlation in time 
which violates the assumption of the Kalman Filter that you have white 
noise. In some cases that correlation does not matter if your data is 
very noisy and the process you are trying to estimate is very slowly 
changing. (In your case the change should be not significant within 60 
measurements than you are ok with your method)
If you want to keep your windowing and use a forgetting factor you 
should use a different window. Google for weighted averages...

>
>Since my experience with Kalman filters includes no moving windows, 
>unless you're doing some serious smoothing, all I can do is point you to

>the book.
>
>You may want to answer my question about how what you're doing compares 
>to the Wikipedia article -- you don't seem to be realizing a standard 
>Kalman filter, hence any questions you ask should have a _lot_ more 
>background on what you really are doing.
>
>-- 
>http://www.wescottdesign.com
>

I may need to have a talk with my teacher to answer you question. Thank
You

>
>One possibility is to update the covariance estimate in the following
>manner
>R(n) =3D (1-lambda)R(n-1) + lambda * x*x'
>
>where lambda is a scalar and x is a vector and x' is the transpose of
>x.
>
>Hope that helps.
>
>Cheers,
>David
>

Do you mind to tell me how do you derive such a euqations and what the
symbols represent for?
THX

On Tue, 07 Apr 2009 20:31:16 GMT, Ben Jackson <ben@ben.com> wrote:

>On 2009-04-07, Muzaffer Kal <kal@dspia.com> wrote:
>> On Tue, 07 Apr 2009 09:59:06 -0500, "adrian116"
>><adrianchang116@hotmail.com> wrote:
>>>>One possibility is to update the covariance estimate in the following
>>>>manner
>>>>R(n) =3D (1-lambda)R(n-1) + lambda * x*x'
>>>>
>>>
>>>Thanks David, what is the 3D in the equation?
>>
>> There is no 3D in the equation. That's your news client adding some
>> markup to the text in the post.
>
>Actually it's *your* news client doign [SIC] MIME encoding and his client

I think your reply is to the wrong person.

On 2009-04-07, Muzaffer Kal <kal@dspia.com> wrote:
> On Tue, 07 Apr 2009 09:59:06 -0500, "adrian116"
><adrianchang116@hotmail.com> wrote:
>>>One possibility is to update the covariance estimate in the following
>>>manner
>>>R(n) =3D (1-lambda)R(n-1) + lambda * x*x'
>>>
>>
>>Thanks David, what is the 3D in the equation?
>
> There is no 3D in the equation. That's your news client adding some
> markup to the text in the post.

Actually it's *your* news client doign MIME encoding and his client
displaying plain text.  The equals sign is the escape character, so
to encode it it uses the hex value of the equal sign, 3D.  So if you
ever see '= 3 D' (but with no spaces) then the original text had just =.

-- 
Ben Jackson AD7GD
<ben@ben.com>
http://www.ben.com/

On Apr 7, 11:58&#4294967295;pm, "adrian116" <adrianchang...@hotmail.com> wrote:
> Dear all,
>
> &#4294967295; &#4294967295; &#4294967295; &#4294967295;I am doing a final year project to estimate the power consumption
> of a chiller plant. I have built a basic model by using Kalman Filter and
> get a result with some percent of error.
>
> For my model, I used a moving windows say 60 sets of data, then calculated
> the parameters to be estimated from these data sets. Use these a,b,c,d,e
> and the measurement of 61set of data to estimation the power at t=61. So
> a,b,c,d,e calculated from 2-61sets of data sets and measurement of 62 set
> of data are used to determine the power at t=62 and so on.
>
> For this case, I need to handle all 60 sets in the moving windows each
> time. It seems strange and time consumption.
>
> My teacher asked me try to introduce forgetting factor to my model. But i
> have searched many material in the web and didn't find any help. Do you
> guys have any idea?
>
> Thank You.

For get ordinary Kalman estimators and use H Infinity estimators -
more robust -

On Tue, 07 Apr 2009 10:02:22 -0500, adrian116 wrote:

>>On Tue, 07 Apr 2009 06:58:30 -0500, adrian116 wrote:
>>
>>> Dear all,
>>> 
>>>        I am doing a final year project to estimate the power
> consumption
>>> of a chiller plant. I have built a basic model by using Kalman Filter
>>> and get a result with some percent of error.
>>> 
>>> For my model, I used a moving windows say 60 sets of data, then
>>> calculated the parameters to be estimated from these data sets. Use
>>> these a,b,c,d,e and the measurement of 61set of data to estimation the
>>> power at t=61. So a,b,c,d,e calculated from 2-61sets of data sets and
>>> measurement of 62 set of data are used to determine the power at t=62
>>> and so on.
>>> 
>>> For this case, I need to handle all 60 sets in the moving windows each
>>> time. It seems strange and time consumption.
>>> 
>>> My teacher asked me try to introduce forgetting factor to my model.
> But
>>> i have searched many material in the web and didn't find any help. Do
>>> you guys have any idea?
>>> 
>>> Thank You.
>>
>>The Kalman filter that I know doesn't involve 'moving windows'; it
>>updates it's state and covariances each iteration, and only requires
>>that
> 
>>you save state and covariance.  How does what you're doing compare to
>>the
> 
>>construction of the Kalman filter in Wikipedia?
>>
>>One quick way to introduce a forgetting factor into a Kalman filter (if
>>it's the one _I_ know, at least!) is to model your system with extra
>>process noise.  This will make the filter pay more attention to it's
>>feedback and less to prior state estimates.
>>
>>If you have time, read a book on Kalman filtering.  I like Dan Simon's
>>"Optimal State Estimation" -- it's a very good text on Kalman filtering.
>>
>>--
>>http://www.wescottdesign.com
>>
>>
> Thank for your reply.
> 
> Actually i don't have much time to go through all the concept of kalman
> filter. Do you mind to tell me which part of the book is critical for my
> application?

Since my experience with Kalman filters includes no moving windows, 
unless you're doing some serious smoothing, all I can do is point you to 
the book.

You may want to answer my question about how what you're doing compares 
to the Wikipedia article -- you don't seem to be realizing a standard 
Kalman filter, hence any questions you ask should have a _lot_ more 
background on what you really are doing.

-- 
http://www.wescottdesign.com

On Tue, 07 Apr 2009 06:58:30 -0500, "adrian116"
<adrianchang116@hotmail.com> wrote:

>Dear all, 
>
>       I am doing a final year project to estimate the power consumption
>of a chiller plant. I have built a basic model by using Kalman Filter and
>get a result with some percent of error.
>
>For my model, I used a moving windows say 60 sets of data, then calculated
>the parameters to be estimated from these data sets. Use these a,b,c,d,e
>and the measurement of 61set of data to estimation the power at t=61. So
>a,b,c,d,e calculated from 2-61sets of data sets and measurement of 62 set
>of data are used to determine the power at t=62 and so on.
>
>For this case, I need to handle all 60 sets in the moving windows each
>time. It seems strange and time consumption.
>
>My teacher asked me try to introduce forgetting factor to my model. But i
>have searched many material in the web and didn't find any help. Do you
>guys have any idea?
>
>Thank You.
>

Normally Kalman filters run on continuous streams of data and not
windows of it. To simulate a forgetting forget you can increase the
system noise variance so that the kalman filter depends more on the
measurement and less on its own estimate. 

Muzaffer Kal

DSPIA INC.
ASIC/FPGA Design Services
http://www.dspia.com

On Tue, 07 Apr 2009 09:59:06 -0500, "adrian116"
<adrianchang116@hotmail.com> wrote:
>>One possibility is to update the covariance estimate in the following
>>manner
>>R(n) =3D (1-lambda)R(n-1) + lambda * x*x'
>>
>>where lambda is a scalar and x is a vector and x' is the transpose of
>>x.
>>
>>Hope that helps.
>>
>>Cheers,
>>David
>>
>
>
>Thanks David, what is the 3D in the equation?

There is no 3D in the equation. That's your news client adding some
markup to the text in the post.

Muzaffer Kal

DSPIA INC.
ASIC/FPGA Design Services
http://www.dspia.com

>On Tue, 07 Apr 2009 06:58:30 -0500, adrian116 wrote:
>
>> Dear all,
>> 
>>        I am doing a final year project to estimate the power
consumption
>> of a chiller plant. I have built a basic model by using Kalman Filter
>> and get a result with some percent of error.
>> 
>> For my model, I used a moving windows say 60 sets of data, then
>> calculated the parameters to be estimated from these data sets. Use
>> these a,b,c,d,e and the measurement of 61set of data to estimation the
>> power at t=61. So a,b,c,d,e calculated from 2-61sets of data sets and
>> measurement of 62 set of data are used to determine the power at t=62
>> and so on.
>> 
>> For this case, I need to handle all 60 sets in the moving windows each
>> time. It seems strange and time consumption.
>> 
>> My teacher asked me try to introduce forgetting factor to my model.
But
>> i have searched many material in the web and didn't find any help. Do
>> you guys have any idea?
>> 
>> Thank You.
>
>The Kalman filter that I know doesn't involve 'moving windows'; it 
>updates it's state and covariances each iteration, and only requires that

>you save state and covariance.  How does what you're doing compare to the

>construction of the Kalman filter in Wikipedia?
>
>One quick way to introduce a forgetting factor into a Kalman filter (if 
>it's the one _I_ know, at least!) is to model your system with extra 
>process noise.  This will make the filter pay more attention to it's 
>feedback and less to prior state estimates.
>
>If you have time, read a book on Kalman filtering.  I like Dan Simon's 
>"Optimal State Estimation" -- it's a very good text on Kalman filtering.
>
>-- 
>http://www.wescottdesign.com
>

Thank for your reply.

Actually i don't have much time to go through all the concept of kalman
filter. Do you mind to tell me which part of the book is critical for my
application?