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Local models-based regression trees for very short-term wind speed prediction
- Source :
- idUS. Depósito de Investigación de la Universidad de Sevilla, instname
- Publication Year :
- 2015
- Publisher :
- Elsevier BV, 2015.
-
Abstract
- This paper evaluates the performance of different types of Regression Trees (RTs) in a real problem of very short-term wind speed prediction from measuring data in wind farms. RT is a solidly established methodology that, contrary to other soft-computing approaches, has been under-explored in problems of wind speed prediction in wind farms. In this paper we comparatively evaluate eight different types of RTs algorithms, and we show that they are able obtain excellent results in real problems of very short-term wind speed prediction, improving existing classical and soft-computing approaches such as multi-linear regression approaches, different types of neural networks and support vector regression algorithms in this problem.We also show that RTs have a very small computation time, that allows the retraining of the algorithms whenever new wind speed data are collected from the measuring towers. Ministerio de Ciencia y Tecnología ECO2010-22065-C03-02 Ministerio de Ciencia y Tecnología TIN2011-28956-C02 Junta de Andalucía P12-TIC-1728 Universidad Pablo de Olavide APPB813097
- Subjects :
- Engineering
Artificial neural network
Renewable Energy, Sustainability and the Environment
business.industry
Astrophysics::High Energy Astrophysical Phenomena
Computation
regression trees
computer.software_genre
Wind speed prediction
Wind speed
Regression
Term (time)
Support vector machine
Very short-term forecasting horizon
Data mining
business
computer
Simulation
Subjects
Details
- ISSN :
- 09601481
- Volume :
- 81
- Database :
- OpenAIRE
- Journal :
- Renewable Energy
- Accession number :
- edsair.doi.dedup.....7a3dca915fd23436fe94b32274c79a2a
- Full Text :
- https://doi.org/10.1016/j.renene.2015.03.071