Work

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

Year: 2015

Type: article

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.

Source: Energies

DOI: 10.3390/en81112361

Authors: Francisco Martínez-Álvarez, Alicia Troncoso, Gualberto Asencio-Cortés, José Riquelme

Institutions: Universidad Pablo de Olavide, Universidad de Sevilla

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: Ministerio de Economía y Competitividad, Junta de Andalucía