A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

Francisco Martínez-Álvarez, Alicia Troncoso, Gualberto Asencio-Cortés and José C. Riquelme
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Francisco Martínez-Álvarez: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Alicia Troncoso: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Gualberto Asencio-Cortés: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
José C. Riquelme: Department of Computer Science, University of Seville, 41012 Seville, Spain

Energies, 2015, vol. 8, issue 11, 1-32

Abstract: Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.

Keywords: energy; time series; forecasting; data mining (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: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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