Forecasting Electricity Price Spikes Using Support Vector Machines

27 Pages Posted: 22 Jun 2017 Last revised: 19 Jul 2018

See all articles by Efthymios Stathakis

Efthymios Stathakis

Democritus University of Thrace - Department of Economics

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace

Periklis Gogas

Democritus University of Thrace - Department of Economics

Date Written: June 21, 2017

Abstract

Electricity markets are considered to be, the most volatile amongst commodity markets. The non-storability of electricity and the need for instantaneous balancing of demand and supply can often cause extreme short-lived fluctuations in electricity prices. These fluctuations are termed price spikes. In this paper, we employ a multiclass Support Vector Machine (SVM) model to forecast the occurrence of price spikes in the German electricity market. As price spikes, we define the prices that lie above the 95th quantile estimated by fitting a Generalized Pareto (GP) distribution in the innovation distribution of an AR-EGARCH model.

Keywords: electricity prices, extreme value theory, exponential GARCH, multiclass, support vector machines

JEL Classification: Q41, C38, C45, C53

Suggested Citation

Stathakis, Efthymios and Papadimitriou, Theophilos and Gogas, Periklis, Forecasting Electricity Price Spikes Using Support Vector Machines (June 21, 2017). Available at SSRN: https://ssrn.com/abstract=2990407 or http://dx.doi.org/10.2139/ssrn.2990407

Efthymios Stathakis (Contact Author)

Democritus University of Thrace - Department of Economics ( email )

Komotini
Greece

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

HOME PAGE: http://econ.duth.gr/author/papadimi/

Periklis Gogas

Democritus University of Thrace - Department of Economics ( email )

Komotini, 69100
Greece

HOME PAGE: http://www.econ.duth.gr/personel/dep/gkogkas/index.en.shtml

Register to save articles to
your library

Register

Paper statistics

Downloads
55
Abstract Views
303
rank
367,339
PlumX Metrics