dc.contributor.author | Duffner, Stephan | |
dc.date.accessioned | 2012-08-15T10:13:21Z | |
dc.date.available | 2012-08-15T10:13:21Z | |
dc.date.issued | 2012 | |
dc.identifier.uri | http://hdl.handle.net/11250/169717 | |
dc.description.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. | no_NO |
dc.language.iso | eng | no_NO |
dc.subject | energy, natural resources and the environment | |
dc.title | Forecasting German day-ahead electricity prices using multivariate time series models | no_NO |
dc.type | Master thesis | no_NO |
dc.subject.nsi | VDP::Technology: 500::Electrotechnical disciplines: 540::Electrical power engineering: 542 | no_NO |