Using a newly available dataset about the unavailability of power plants and the in-feed ofrenewable energies to forecast day-ahead electricity prices at the German Power Exchange,this work shows that the predictive power increases considerably when includingexogenous variables. While a similar univariate approach based on the year 2001 yielded aMean Absolute Percentage Error of 13.2%, the use of the presented variables improved theforecasting error to 8.3%. Other findings of this work include that a model based on 24individual time series produces smaller forecasting errors than one time series whichincludes all consecutive hours, that the selection of the in-sample and out-of-sampleperiods varies greatly between different works and that the use of OLS seems to beunderestimated in the existing forecasting literature for electricity prices.
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