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

1
Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
2
Department of Computer Science, University of Seville, 41012 Seville, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: John Ringwood
Energies 2015, 8(11), 13162-13193; https://doi.org/10.3390/en81112361
Received: 16 July 2015 / Revised: 24 September 2015 / Accepted: 6 November 2015 / Published: 19 November 2015
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. View Full-Text
Keywords: energy; time series; forecasting; data mining energy; time series; forecasting; data mining
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MDPI and ACS Style

Martínez-Álvarez, F.; Troncoso, A.; Asencio-Cortés, G.; Riquelme, J.C. A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting. Energies 2015, 8, 13162-13193. https://doi.org/10.3390/en81112361

AMA Style

Martínez-Álvarez F, Troncoso A, Asencio-Cortés G, Riquelme JC. A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting. Energies. 2015; 8(11):13162-13193. https://doi.org/10.3390/en81112361

Chicago/Turabian Style

Martínez-Álvarez, Francisco, Alicia Troncoso, Gualberto Asencio-Cortés, and José C. Riquelme 2015. "A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting" Energies 8, no. 11: 13162-13193. https://doi.org/10.3390/en81112361

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