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

Energies

Francisco Martínez-Álvarez²  Alicia Troncoso²  Gualberto Asencio-Cortés²  José C. Riquelme¹ 

[1] Department of Computer Science, University of Seville, 41012 Seville, Spain;Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain;

关键词: energy;    time series;    forecasting;    data mining;   

DOI  :  10.3390/en81112361

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.