Electricity Price Forecasting with Dynamic Trees: A Benchmark Against the Random Forest Approach

Javier Pórtoles, Camino González and Javier M. Moguerza
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Javier Pórtoles: Doctorate Programme in Information Technologies and Communications, University Rey Juan Carlos, c/ Tulipán s/n, 28933 Móstoles, Spain
Camino González: Statistical Laboratory, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, c/ José Gutiérrez Abascal, 2, 28006 Madrid, Spain
Javier M. Moguerza: Data Science Laboratory, University Rey Juan Carlos, c/ Tulipán s/n, 28933 Móstoles, Spain

Energies, 2018, vol. 11, issue 6, 1-21

Abstract: Dynamic Trees are a tree-based machine learning technique specially designed for online environments where data are to be analyzed sequentially as they arrive. Our purpose is to test this methodology for the very first time for Electricity Price Forecasting (EPF) by using data from the Iberian market. For benchmarking the results, we will compare them against another tree-based technique, Random Forest, a widely used method that has proven its good results in many fields. The benchmark includes several versions of the Dynamic Trees approach for a very short term EPF (one-hour ahead) and also a short term (one-day ahead) approach but only with the best versions. The numerical results show that Dynamic Trees are an adequate method, both for very short and short term EPF—even improving upon the performance of the Random Forest method. The comparison with other studies for the Iberian market suggests that Dynamic Trees is a proper and promising method for EPF.

Keywords: electricity price forecasting; artificial intelligence; dynamic trees; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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