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

Permanent URI for this collection

http://hdl.handle.net/2454/10493

Browse

Now showing 1 - 6 of 6

Open Access
Exploring the limits of wind farm grouping for prediction error compensation
(European Wind Energy Association, 2006) Gastón Romeo, Martín; Frías Paredes, Laura; Martí, Ignacio; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
Open Access
Implementation of artificial intelligence algorithms in climatic zoning according with energy demand in dwellings. A european case
(2021) Llorente Yoldi, Javier; Gastón Romeo, Martín; Frías Paredes, Laura; Ur Rehman, Hassam; Estadística e Investigación Operativa; Institute of Smart Cities - ISC; Estatistika eta Ikerketa Operatiboa
Science has shown, based on data and knowledge, that there is clear evidence that global warming is not a transient effect, and that it is caused mainly by human activity. The consequences of climate change can be noted and quantified nowadays, but the worst thing is that these impacts are expected to intensify in the coming decades. Obviously, global problems require global solutions, and even if there are certain difficulties to define a clear path, there is no doubt that the worldwide efforts should lead to keep the global temperatura increase to well below 1.5ºC
Open 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.
Open Access
Wind power predictability assessment from large to local scale
(European Wind Energy Association, 2013) Rodrigo, J.; Frías Paredes, Laura; Stoffels, Nicole; Bremen, Lueder von; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Large wind power penetration and efficient wind farm operation require early consideration of the impact of wind power predictability from site to regional/country and continental levels. The aim of this work is to explore the added value of introducing wind power predictability in the wind resource assessment phase for large scale spatial planning and for wind farm performance evaluation. To this end, historical wind data are converted to virtual time series of wind power production on which forecasting models operate in hindcast mode to estimate predictability information. This information can be used to anticipate operational costs and lead to a better assessment of the cost-benefit of wind energy deployment. Numerical weather prediction models give us the opportunity to evaluate wind power predictability and other forecast skills. The COSMO-EU mesoscale model wind speed data is used to create a spatial dataset of different forecast skills and visualize those in form of European predictability maps at a resolution of 7×4 km. In addition to a large capacity factor, low wind variability is also desirable for smooth wind power integration. A smoothing factor determines the areas that benefit most from wind farm aggregation resulting in increased predictability and reduced variability. These forecast skill maps provide useful information for spatial planning and can be analyzed to determine the sources of forecasting errors, either by topographical effects or meteorological phenomena. At site level, wind measurements from a reference mast are converted into wind farm production time series making use of a microscale flow model. Then CENER's LocalPred wind power forecasting model is run, emulating operational conditions to assess predictability. A test case comprising two wind farms in Spain of different terrain complexity, equipped with measurements from the resource assessment and the operational phases, is presented to illustrate a methodology for local predictability assessment. Low wind power predictability is certainly a good indicator of quality-of-energy for grid integration. Hence, this type of information should be included during the development phase of a wind farm as complementary information about its performance. In conclusion, guidelines on how to produce and evaluate wind power predictability during the planning phase are provided in order to advise end-users on the use of these new layers of information.
Open 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.
Open 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.

Browse

Browsing Comunicaciones y ponencias de congresos DEIO - EIOS Biltzarretako komunikazioak eta txostenak by Author "Frías Paredes, Laura"

Results Per Page

Sort Options