Comunicaciones y ponencias de congresos DEIO - EIOS Biltzarretako komunikazioak eta txostenak

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Browsing Comunicaciones y ponencias de congresos DEIO - EIOS Biltzarretako komunikazioak eta txostenak by Author "Cantero Nouqueret, Elena"

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    PublicationOpen Access
    Support vector machines in the wind energy framework: a new model for wind energy forecasting
    (European Wind Energy Association, 2009) Frías Paredes, Laura; Pascal, Edurne; Irigoyen, Uxue; Cantero Nouqueret, Elena; Loureiro, Yolanda; Lozano, Sergio; Fernandes Correia, Pedro Miguel; Martí, Ignacio; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    In this work we expose a new model for wind energy forecasting based in Support Vector Machines. Support vector machines (SVMs) are a group of supervised learning methods that can be applied to classification or regression. They represent an extension to nonlinear models of the generalized portrait algorithm developed by Vladimir Vapnik. Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers in the context of Vapnik's statistical learning theory. Since then SVMs have been successfully applied to real-world data analysis problems, often providing improved results compared with other techniques. The SVMs operate within the framework of regularization theory by minimizing an empirical risk in a well-posed and consistent way. A clear advantage of the support vector approach is that sparse solutions to classification and regression problems are usually obtained: only a few samples are involved in the determination of the classification or regression functions. This fact facilitates the application of SVMs to problems that involve a large amount of data. Joint to that, the use of kernel functions into their algorithms allows the adaptability to nonlinear problems of SVMs. There are many "traditional" problems in the wind power forecasting systems that affect the accuracy and robustness of the forecasts, as availability of real power measures, the existence of wrong data and the nonlinearity of the relationship between meteorological variables and power. So, these robustness and adaptability of SVMs suggest that these types of learning machines could be a good set of tools to solve many problems in the wind energy context.
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    PublicationOpen Access
    Wind resources map of Iberian Peninsula using a mesoscale model: comparison of different methodologies
    (European Wind Energy Association, 2009) Lozano, Sergio; Irigoyen, Uxue; Martí, Ignacio; Cantero Nouqueret, Elena; Pascal, Edurne; Fernandez, P.; Frías Paredes, Laura; Garciandia, J.; Zubillaga, E.; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
    With an increasing number of wind farms planned in multiple locations without wind resource information, that comprise entire countries, there is a need for long term wind assessment, for both onshore and offshore wind resource potential. The focus of this work is to find the optimum methodology to build a wind map taking into account not only accuracy, but also the computational requirements. For this purpose six different wind resources maps have been calculated for Spain using a mesoscale model (SKIRON). The wind maps were calculated with different inputs and model configuration. An extensive validation was carried out using 24 meteorological station.
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    PublicationOpen Access
    Wind resources map of Spain at mesoscale: methodology and validation
    (European Wind Energy Association, 2008) Gastón Romeo, Martín; Pascal, Edurne; Frías Paredes, Laura; Martí, Ignacio; Irigoyen, Uxue; Cantero Nouqueret, Elena; Lozano, Sergio; Loureiro, Yolanda; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
    A wind resource map of Spain has been built using the mesoscale model Skiron. It covers all the Iberian geography. To measure the accuracy level of this map, a protocol of validation has been developed. It provides global information as well as reports about dierent regions, levels of wind speed, direction sector etc. A validation of the map using 50 meteorological stations is presented.