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Automatisk viktning av onlinekorrigering för prognoser

Rosén, Erik (2013) FMS820 20131
Mathematical Statistics
Abstract (Swedish)
This report aims at finding methods for reducing the errors
in district heating load forecasts by suggesting a method for
online correction of the base forecast. Online correction is
generally carried out by utilizing patterns in the historic
prediction errors. Traditionally the correction has been
made using a correction method detailed by a few governing
parameters. Values for these parameters are set according
to the experience of energy engineers. One of the major
topics for this report will be to choose these parameters
automatically and in an online fashion using historic error
data. Another approach to the correction problem is made
by modelling the errors from scratch. Starting from a time
series analysis of the error... (More)
This report aims at finding methods for reducing the errors
in district heating load forecasts by suggesting a method for
online correction of the base forecast. Online correction is
generally carried out by utilizing patterns in the historic
prediction errors. Traditionally the correction has been
made using a correction method detailed by a few governing
parameters. Values for these parameters are set according
to the experience of energy engineers. One of the major
topics for this report will be to choose these parameters
automatically and in an online fashion using historic error
data. Another approach to the correction problem is made
by modelling the errors from scratch. Starting from a time
series analysis of the error series all significant linear dependencies
can be identified and different models from the
Auto Regressive Moving Average (ARMA) family of models
are evaluated. It is found that purely Auto Regressive
models will give the best results for these error series. In
the error series there are clearly visible non-linear phenomena
present i.e. a slightly varying mean level as well as a
more heavily varying variance. By putting AR-models with
varying mean and variance on a nonlinear state space form
an attempt is made at handling this. The AR-parameters,
the mean level and the varying variance is then estimated
using an Unscented Kalman Filter. The report is written
in Swedish. (Less)
Please use this url to cite or link to this publication:
author
Rosén, Erik
supervisor
organization
course
FMS820 20131
year
type
H2 - Master's Degree (Two Years)
subject
language
Swedish
id
3994606
date added to LUP
2013-08-23 09:14:11
date last changed
2013-08-23 09:14:11
@misc{3994606,
  abstract     = {This report aims at finding methods for reducing the errors
in district heating load forecasts by suggesting a method for
online correction of the base forecast. Online correction is
generally carried out by utilizing patterns in the historic
prediction errors. Traditionally the correction has been
made using a correction method detailed by a few governing
parameters. Values for these parameters are set according
to the experience of energy engineers. One of the major
topics for this report will be to choose these parameters
automatically and in an online fashion using historic error
data. Another approach to the correction problem is made
by modelling the errors from scratch. Starting from a time
series analysis of the error series all significant linear dependencies
can be identified and different models from the
Auto Regressive Moving Average (ARMA) family of models
are evaluated. It is found that purely Auto Regressive
models will give the best results for these error series. In
the error series there are clearly visible non-linear phenomena
present i.e. a slightly varying mean level as well as a
more heavily varying variance. By putting AR-models with
varying mean and variance on a nonlinear state space form
an attempt is made at handling this. The AR-parameters,
the mean level and the varying variance is then estimated
using an Unscented Kalman Filter. The report is written
in Swedish.},
  author       = {Rosén, Erik},
  language     = {swe},
  note         = {Student Paper},
  title        = {Automatisk viktning av onlinekorrigering för prognoser},
  year         = {2013},
}