Forecasting Electricity Prices Using Nonlinear Method

Abstract

The goal of this paper is to analyze the electricity prices using chaos theory and to predict using nonlinear method. At first we estimated the time delay and the embedding dimension, which is needed for the Lyapunov exponent estimation and for the phase space reconstruction. Subsequently, we computed the largest Lyapunov exponent, which is one of the important indicators of chaos. The results indicated that chaotic behaviors obviously exist in electricity price series. If the system behaves chaotically, we are forced to accept limited predictions. Finally we computed predictions using a radial basis function to fit global nonlinear functions to the data. In this paper we analyze electricity price series of the biggest European energy markets EEX (Central European Energy Exchange).