Forecasting energy markets using support vector machines

Theophilos Papadimitriou, Periklis Gogas and Efthymios Stathakis

Energy Economics, 2014, vol. 44, issue C, 135-142

Abstract: In this paper we investigate the efficiency of a support vector machine (SVM)-based forecasting model for the next-day directional change of electricity prices. We first adjust the best autoregressive SVM model and then we enhance it with various related variables. The system is tested on the daily Phelix index of the German and Austrian control area of the European Energy Exchange (ΕΕΧ) wholesale electricity market. The forecast accuracy we achieved is 76.12% over a 200day period.

Keywords: Support vector machines; Autoregressive model; European Energy Exchange; Day-ahead market (search for similar items in EconPapers)
JEL-codes: C45 C51 E44 G10 G17 Q43 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:44:y:2014:i:c:p:135-142

DOI: 10.1016/j.eneco.2014.03.017

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