Forecasting electricity consumption using nonlinear projection and self-organizing maps

Lendasse, Amaury;Lee, John;Wertz, Vincent;Verleysen, Michel
(2002) 8th European Symposium on Artificial Neural Networks (ESANN) — Location: BRUGGE(Belgium) (26.April.2001)

Files

J14-Forecastingelectricityconsumptionusingnonlinearprojectionandself-organizingmaps.pdf
  • Restricted Access
  • Adobe PDF
  • 335.36 KB
Request a copy

Details

Authors
Abstract
A general-purpose useful parameter in time series forecasting is the regressor size, corresponding to the minimum number of variables necessary to forecast the future values of the time series. If the models are nonlinear, the choice of this regressor becomes very difficult. We present a quasi-automatic method using a nonlinear projection named curvilinear component analysis to build this regressor. The size of this regressor will be determined by the estimation of the intrinsic dimension of an over-sized regressor. This method will be applied to electric consumption of Poland using systematic cross-validation. The nonlinear model used for the prediction is a Kohonen map (self-organizing map). (C) 2002 Published by Elsevier Science B.V.
Affiliations

Citations

  • APA
  • Chicago
  • FWB

Lendasse, A., Lee, J., Wertz, V., & Verleysen, M. (2002). Forecasting electricity consumption using nonlinear projection and self-organizing maps. Neurocomputing, 48, 299-311. https://hdl.handle.net/2078.5/254036 (Original work published 2002)