Comparison of several measure-correlate-predict models using support vector regression techniques to estimate wind power densities. A case study-Reference-Cited by-同舟云学术

Comparison of several measure-correlate-predict models using support vector regression techniques to estimate wind power densities. A case study

Published:2017-05 Issue: Volume:140 Page:334-354
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Díaz Santiago,Carta José A.,Matías José M.

Funder

State Meteorological Agency of the Ministry of Agriculture, Food and Environment of the Government of Spain

Canary Technological Institute

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Reference58 articles.

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3. Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site;Carta;Energy Convers Manage,2011

4. A hybrid measure–correlate–predict method for long-term wind condition assessment;Zhang;Energy Convers Manage,2014

5. Patanè D, Benso M, Hernández C, deLaBlanca F, López C. Long term wind resource assessment by means of multivariate cross-correlation analysis. Paper number: 1452. In: Proceedings of the European wind energy conference and exhibition. Brussels, Belgium; 14–17 March 2011.

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