Fakultät für Mathematik und Naturwissenschaften

Applying Multivariate Statistical Methods for Forecasting Electricity Price Contributors

Partner: Universität Wuppertal Lehrstuhl Stochastik, Vattenfall Energy Trading GmbH

Task: Price contributors to be considered:

  • Import/Export (cross-border) flow of electricity in Germany
  • Margins/add-ons

The task is to come up with better price contributor models for Germany. The approach to be investigated is a reference day/hour method. This approach is to be investigated because we think it can give better results than we have, and we have never made a thorough investigation of this approach before.

This approach is then to be compared with the present models; Principal Component Regression, PCR for margins, and Partial Least Squares regression for import/export short-term forecast and weekly mean regression f or import/export long-term forecast.

Reference day model: The aim of this method is to find historically similar days/hours by studying factors (demand, wind, etc), that affect the price contributors, and then the value(s) of the fundamentally equal day /hour or days /hours is reused.

The steps to be undertaken can roughly be explained as follows:

  1. Find a suitable set of statistically significant underlying factors (such as wind production, demand, temperature, holidays, etc) which can explain historical price contributors well enough.
  2. Investigate robust method of comparing daily/hourly price contributor to each other. Practically, single hours and single days are most likely to be compared. Linear and non-linear methods can be taken into consideration. For example discriminant analysis and Classification and Regression Trees (CART).
  3. Estimate future price contributors using the methods from point 1-2 above, by comparing the future predictors (such as wind production, demand, temperature, holidays, etc) to their past values and thereby finding the most similar historic/observed price contributor.
  4. The forecast results are then compared with the results from the present models.

Kooperationspartner: Vattenfall (Website)

Weitere Infos über #UniWuppertal: