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Analysis of Forecasts for District Heat Production using Different Models for Seasonal Partitions

Einarsson, Leo LU (2021) In Master's thesis in Matematical Scieces FMSM01 20211
Mathematical Statistics
Abstract
District heating is a common means of space and hot water heating
in Sweden. However, the demand for heating is not the same at all times.
On a yearly basis more heat is required during winter, while next to none
is needed in summer. Since the demand for heat load varies throughout the
year, when trying to predict it, using a model that changes with the seasons
can give a more accurate prediction. In this study, a forecasting model was
tested to change its parameters either yearly, every three months (seasonal),
monthly or weekly. The goal was to see which way of partitioning the year
would give a more reliable prediction. Using statistical bootstrap to create
confidence and prediction bands for the heat load, an analysis was... (More)
District heating is a common means of space and hot water heating
in Sweden. However, the demand for heating is not the same at all times.
On a yearly basis more heat is required during winter, while next to none
is needed in summer. Since the demand for heat load varies throughout the
year, when trying to predict it, using a model that changes with the seasons
can give a more accurate prediction. In this study, a forecasting model was
tested to change its parameters either yearly, every three months (seasonal),
monthly or weekly. The goal was to see which way of partitioning the year
would give a more reliable prediction. Using statistical bootstrap to create
confidence and prediction bands for the heat load, an analysis was conducted.
The results show that a seasonal or monthly approach give a more accurate
prediction overall and that the summer was most difficult to predict, relative
to the produced heat, although transition seasons, for instance between spring
and summer were more prone to large variances overall. (Less)
Please use this url to cite or link to this publication:
author
Einarsson, Leo LU
supervisor
organization
alternative title
Analys av Fjärrvärmeprognoser med Olika Modeller Beroende på Tid på Året
course
FMSM01 20211
year
type
H2 - Master's Degree (Two Years)
subject
keywords
District heating, heat production, energy, time series, statistical bootstrap, prediction, regression, model based on season
publication/series
Master's thesis in Matematical Scieces
report number
LUTFMS-3425-2021
ISSN
1404-6342
other publication id
2021:E50
language
English
id
9057756
date added to LUP
2021-07-02 16:09:04
date last changed
2021-07-02 16:09:04
@misc{9057756,
  abstract     = {{District heating is a common means of space and hot water heating
in Sweden. However, the demand for heating is not the same at all times.
On a yearly basis more heat is required during winter, while next to none
is needed in summer. Since the demand for heat load varies throughout the
year, when trying to predict it, using a model that changes with the seasons
can give a more accurate prediction. In this study, a forecasting model was
tested to change its parameters either yearly, every three months (seasonal),
monthly or weekly. The goal was to see which way of partitioning the year
would give a more reliable prediction. Using statistical bootstrap to create
confidence and prediction bands for the heat load, an analysis was conducted.
The results show that a seasonal or monthly approach give a more accurate
prediction overall and that the summer was most difficult to predict, relative
to the produced heat, although transition seasons, for instance between spring
and summer were more prone to large variances overall.}},
  author       = {{Einarsson, Leo}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's thesis in Matematical Scieces}},
  title        = {{Analysis of Forecasts for District Heat Production using Different Models for Seasonal Partitions}},
  year         = {{2021}},
}