• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Norges Handelshøyskole
  • Thesis
  • Master Thesis
  • View Item
  •   Home
  • Norges Handelshøyskole
  • Thesis
  • Master Thesis
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Forecasting German day-ahead electricity prices using multivariate time series models

Duffner, Stephan
Master thesis
Thumbnail
View/Open
Duffner 2012.pdf (1.933Mb)
URI
http://hdl.handle.net/11250/169717
Date
2012
Metadata
Show full item record
Collections
  • Master Thesis [4207]
Abstract
Using a newly available dataset about the unavailability of power plants and the in-feed of

renewable energies to forecast day-ahead electricity prices at the German Power Exchange,

this work shows that the predictive power increases considerably when including

exogenous variables. While a similar univariate approach based on the year 2001 yielded a

Mean Absolute Percentage Error of 13.2%, the use of the presented variables improved the

forecasting error to 8.3%. Other findings of this work include that a model based on 24

individual time series produces smaller forecasting errors than one time series which

includes all consecutive hours, that the selection of the in-sample and out-of-sample

periods varies greatly between different works and that the use of OLS seems to be

underestimated in the existing forecasting literature for electricity prices.

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit