Efficient Integration of External Information into Forecast Models from the Energy Domain

  • Lars Dannecker
  • Elena Vasilyeva
  • Matthias Boehm
  • Wolfgang Lehner
  • Gregor Hackenbroich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7503)


Forecasting is an important analysis technique to support decisions and functionalities in many application domains. While the employed statistical models often provide a sufficient accuracy, recent developments pose new challenges to the forecasting process. Typically the available time for estimating the forecast models and providing accurate predictions is significantly decreasing. This is especially an issue in the energy domain, where forecast models often consider external influences to provide a high accuracy. As a result, these models exhibit a higher number of parameters, resulting in increased estimation efforts. Also, in the energy domain new measurements are constantly appended to the time series, requiring a continuous adaptation of the models to new developments. This typically involves a parameter re-estimation, which is often almost as expensive as the initial estimation, conflicting with the requirement for fast forecast computation. To address these challenges, we present a framework that allows a more efficient integration of external information. First, external information are handled in a separate model, because their linear and non-linear relationships are more stable and thus, they can be excluded from most forecast model adaptations. Second, we directly optimize the separate model using feature selection and dimension reduction techniques. Our evaluation shows that our approach allows an efficient integration of external information and thus, an increased forecasting accuracy, while reducing the re-estimation efforts.


Forecasting External Information Efficiency 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    MIRABEL Project (2011), http://www.mirabel-project.eu
  2. 2.
    MeRegio Project (2011), http://www.meregio.de/en/
  3. 3.
    Chateau, B., Lapillonne, B.: Long-term energy demand forecasting a new approach. Energy Policy 6(2), 140–157 (1978)CrossRefGoogle Scholar
  4. 4.
    Hor, C.L., Watson, S., Majithia, S.: Analyzing the impact of weather variables on monthly electricity demands. Power Systems 20(4), 2078–2085 (2005)CrossRefGoogle Scholar
  5. 5.
    Ruźić, S., Vuckovic, A., Nikolic, N.: Weather sensitive method for short term load forecasting in electricpower utility of serbia. Power Systems 18(4), 1581–1586 (2003)CrossRefGoogle Scholar
  6. 6.
    Ramanathan, R., Engle, R., Granger, C.W., Vahid-Araghi, F., Brace, C.: Short-run forecasts of electricity loads and peaks. International Journal of Forecasting 13(2), 161–174 (1997)CrossRefGoogle Scholar
  7. 7.
    Spearman, C.: The proff and measurement of association between two thins. American Journal of Psychology 15, 72–101 (1904)CrossRefGoogle Scholar
  8. 8.
    Pearson, K.: On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2(6), 559–572 (1901)Google Scholar
  9. 9.
    Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer Series in Statistics. Springer Verlag Inc. (2002)Google Scholar
  10. 10.
    Taylor, J.W.: Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research 204, 139–152 (2009)CrossRefGoogle Scholar
  11. 11.
    Center for Renewable Energy Sources and Saving (2012), http://www.cres.gr/kape/index_eng.htm
  12. 12.
    Deutscher Wetterdienst (2012), http://www.dwd.de
  13. 13.
    Nelder, J., Mead, R.: A simplex method for function minimization. The Computer Journal 7(4), 308–313 (1965)MATHGoogle Scholar
  14. 14.
    Akdere, M., Cetintemel, U., Upfal, E.: Database-support for continuous prediction queries over streaming data. In: VLDB 2010 (2010)Google Scholar
  15. 15.
    Parisi, F., Sliva, A., Subrahmanian, V.S.: Embedding Forecast Operators in Databases. In: Benferhat, S., Grant, J. (eds.) SUM 2011. LNCS, vol. 6929, pp. 373–386. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Duan, S., Babu, S.: Processing forecasting queries. In: VLDB 2007 (2007)Google Scholar
  17. 17.
    Ge, T., Zdonik, S.: A skip-list approach for efficiently processing forecasting queries. In: Proceeding of the VLDB 2008 (2008)Google Scholar
  18. 18.
    Dannecker, L., Schulze, R., Böhm, M., Lehner, W., Hackenbroich, G.: Context-Aware Parameter Estimation for Forecast Models in the Energy Domain. In: Bayard Cushing, J., French, J., Bowers, S. (eds.) SSDBM 2011. LNCS, vol. 6809, pp. 491–508. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control. John Wiley & Sons Inc. (1970)Google Scholar
  20. 20.
    Bruhns, A., Deurveilher, G., Roy, J.S.: A non-linear regression model for mid term load forecasting and improvements in seasonality. In: Proceedings of the 15th PSCC 2005 (2005)Google Scholar
  21. 21.
    Wi, Y.M., Kim, J.H., Sung-Kwan Joo, J.B.P., Oh, J.C.: Customer baseline load (cbl) calculation using exponential smoothingmodel with weather adjustment. In: Proceedings of the 2009 TDCE (2009)Google Scholar
  22. 22.
    Taylor, J.W., de Menezes, L.M., McSharry, P.E.: A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting 22, 1–16 (2006)CrossRefGoogle Scholar
  23. 23.
    Taylor, J.W., McSharry, P.E.: Short-term load forecasting methods: An evaluation based on european data. Power Systems 22, 2213–2219 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lars Dannecker
    • 1
  • Elena Vasilyeva
    • 1
  • Matthias Boehm
    • 2
  • Wolfgang Lehner
    • 2
  • Gregor Hackenbroich
    • 1
  1. 1.SAP Research DresdenDresdenGermany
  2. 2.Database Technology GroupTechnische Universität DresdenDresdenGermany

Personalised recommendations