  Our paper aims to model and forecast the electricity price by taking a
completely new perspective on the data. It will be the first approach which is
able to combine the insights of market structure models with extensive and
modern econometric analysis. Instead of directly modeling the electricity price
as it is usually done in time series or data mining approaches, we model and
utilize its true source: the sale and purchase curves of the electricity
exchange. We will refer to this new model as X-Model, as almost every
deregulated electricity price is simply the result of the intersection of the
electricity supply and demand curve at a certain auction. Therefore we show an
approach to deal with a tremendous amount of auction data, using a subtle data
processing technique as well as dimension reduction and lasso based estimation
methods. We incorporate not only several known features, such as seasonal
behavior or the impact of other processes like renewable energy, but also
completely new elaborated stylized facts of the bidding structure. Our model is
able to capture the non-linear behavior of the electricity price, which is
especially useful for predicting huge price spikes. Using simulation methods we
show how to derive prediction intervals for probabilistic forecasting. We
describe and show the proposed methods for the day-ahead EPEX spot price of
Germany and Austria.
