Analysis of Forecasts for District Heat Production using Different Models for Seasonal Partitions
(2021) In Master's thesis in Matematical Scieces FMSM01 20211Mathematical 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:
http://lup.lub.lu.se/student-papers/record/9057756
- author
- Einarsson, Leo LU
- supervisor
- organization
- alternative title
- Analys av Fjärrvärmeprognoser med Olika Modeller Beroende på Tid på Året
- course
- FMSM01 20211
- year
- 2021
- 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}}, }