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Open AccessEditorial

Recent Advances in Energy Time Series Forecasting

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Department of Computer Science, Pablo de Olavide University, ES-41013 Seville, Spain
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Department of Computer Science, University of Seville, 41012 Seville, Spain
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Author to whom correspondence should be addressed.
Academic Editor: Enrico Sciubba
Energies 2017, 10(6), 809; https://doi.org/10.3390/en10060809
Received: 8 June 2017 / Revised: 12 June 2017 / Accepted: 12 June 2017 / Published: 14 June 2017
(This article belongs to the Special Issue Energy Time Series Forecasting)
This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI’s Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results. View Full-Text
Keywords: energy; time series; forecasting energy; time series; forecasting
MDPI and ACS Style

Martínez-Álvarez, F.; Troncoso, A.; Riquelme, J.C. Recent Advances in Energy Time Series Forecasting. Energies 2017, 10, 809.

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