Title: Two machine learning approaches for short-term wind speed time-series prediction
Authors : Ak, Ronay
Fink, Olga
Zio, Enrico
Published in : IEEE transactions on neural networks and learning systems
Volume(Issue) : 27
Issue : 8
Pages : 1734
Pages to: 1747
Publisher / Ed. Institution : IEEE
Issue Date: 2016
License (according to publishing contract) : Licence according to publishing contract
Type of review: Not specified
Language : English
Subject (DDC) : 004: Computer science
Abstract: The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Publication type: Article in scientific journal
DOI : 10.1109/TNNLS.2015.2418739
ISSN: 2162-237X
2162-2388
URI: https://digitalcollection.zhaw.ch/handle/11475/13904
Appears in Collections:Publikationen School of Engineering

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