Forecasting Electricity Consumption using Non-Linear Projection and Self-Organizing Maps
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). © 2002 Published by Elsevier Science B.v.
Recommended Citation
A. Lendasse et al., "Forecasting Electricity Consumption using Non-Linear Projection and Self-Organizing Maps," Neurocomputing, vol. 48, no. 1 thru 4, pp. 299 - 311, Elsevier, Jan 2002.
The definitive version is available at https://doi.org/10.1016/S0925-2312(01)00646-4
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Curvilinear component analysis; Electricity consumption; Nonlinear projection; Self-organizing map; Time series prediction
International Standard Serial Number (ISSN)
0925-2312
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jan 2002
