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
Analysis of wind power productions by means of an analog model
(Elsevier, 2014) Martín, María Luisa; Valero Rodríguez, Francisco; Pascual, Ananda; Sanz Rodrigo, Javier; Frías Paredes, Laura; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
The purpose of this work is to evaluate the performance of an analog model on day-ahead forecasting of wind power production over large European regions based in Ireland, Denmark and Germany. To do this, several data sets have been used: sea level pressure field over the North Atlantic and wind power outputs from individual wind farms and from wind farm clusters. The analog method uses Principal Component Analysis to reduce the dimensionality of the large-scale atmospheric database. Then, the analog method is based on the finding in the historic sea level pressure database, a principal component subset of large-scale atmospheric patterns that are the most similar to a large-scale atmospheric pattern used as input. Similar atmospheric situations to a particular atmospheric situation to be modeled have been determined and from them, different wind power outputs have been estimated. Several deterministic and probabilistic results are shown. Results of bias, spatial correlations and root mean squared errors between the estimated and observational wind power outputs are displayed. Concerning wind farm data set, the analog method improves both climatology and persistence in the Danish test case. The probabilistic results are shown by means of Brier Skill Scores and reliability diagrams. Danish test case shows pretty good BSS results with underestimation of the observational wind power frequencies in the reliability diagrams. For aggregated data sets, the model performing improves climatology in both Danish and German test cases, showing the latter better results than the former. A comparison between the two Danish databases, wind farm and aggregated data, gives as result higher BSSs for aggregated data than for the wind farm data set in high wind power outputs. The process used in this work to estimate wind power productions based on finding analogs in a previously reduced large-scale atmospheric data has proven to be a good technique to analyze the effect of the regional wind climate contribution to the daily wind output prediction.
Open Access
Early detection of new pandemic waves: control chart and a new surveillance index
(Public Library of Science, 2024) Cildoz Esquíroz, Marta; Gastón Romeo, Martín; Frías Paredes, Laura; García de Vicuña Bilbao, Daniel; Azcárate Camio, Cristina; Mallor Giménez, Fermín; Institute of Smart Cities - ISC
The COVID-19 pandemic highlights the pressing need for constant surveillance, updating of the response plan in post-peak periods and readiness for the possibility of new waves of the pandemic. A short initial period of steady rise in the number of new cases is sometimes followed by one of exponential growth. Systematic public health surveillance of the pandemic should signal an alert in the event of change in epidemic activity within the community to inform public health policy makers of the need to control a potential outbreak. The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with a new surveillance metric to overcome some of their difficulties in capturing the changing dynamics of the pandemic. At statistically-founded threshold values, the new measure will trigger alert signals giving early warning of the onset of a new pandemic wave. We define a new index, the weighted cumulative incidence index, based on the daily new-case count. We model the infection spread rate at two levels, inside and outside homes, which explains the overdispersion observed in the data. The seasonal component of real data, due to the public surveillance system, is incorporated into the statistical analysis. Probabilistic analysis enables the construction of a Control Chart for monitoring index variability and setting automatic alert thresholds for new pandemic waves. Both the new index and the control chart have been implemented with the aid of a computational tool developed in R, and used daily by the Navarre Government (Spain) for virus propagation surveillance during post-peak periods. Automated monitoring generates daily reports showing the areas whose control charts issue an alert. The new index reacts sooner to data trend changes preluding new pandemic waves, than the standard surveillance index based on the 14-day notification rate of reported COVID-19 cases per 100,000 population.
Open Access
I Congreso Salud, Desastres y Desarrollo Sostenible: libro congreso
(2022) Azcárate Camio, Cristina; Cildoz Esquíroz, Marta; Frías Paredes, Laura; Ibarra, Amaia; Galbete Jiménez, Arkaitz; García de Vicuña Bilbao, Daniel; Gastón Romeo, Martín; Moler Cuiral, José Antonio; Mallor Giménez, Fermín; Jean Louis, Clint; Institute of Smart Cities - ISC
El congreso se plantea como un foro de encuentro de investigadores del área de Investigación Operativa con interés en aplicaciones a la salud, los desastres y el desarrollo sostenible, y los profesionales de la toma de decisiones concernientes a los ámbitos anteriores. Este encuentro promueve el intercambio de conocimiento y experiencias entre Universidad y Servicios de Salud para afrontar retos asociados al acceso de la población a unos servicios de salud de calidad y a la gestión del riesgo creciente de desastres naturales o provocados por el ser humano. El envejecimiento de la población y el desarrollo tecnológico plantean nuevos entornos para la provisión de los servicios de salud, en los que su correcta planificación y gestión debe contribuir a garantizar su eficiencia y sostenibilidad. El creciente impacto en términos de vidas humanas y daños económicos causados por desastres naturales y no naturales, como incendios, inundaciones, terremotos, fugas industriales, pandemias, etc. precisa de su comprensión para desarrollar estrategias de prevención y elaborar planes efectivos de respuesta.
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.
Open Access
Local wind speed forecasting based on WRF-HDWind coupling
(Elsevier, 2021-08-28) Prieto-Herráez, Diego; Frías Paredes, Laura; Cascón, J. Manuel; Lagüela, Susana; Gastón Romeo, Martín; Asensio, María Isabel; Martín Nieto, Ignacio; Fernandes Correia, Pedro Miguel; Laiz-Alonso, Pablo; Carrasco-Díaz, O. F.; Sáez Blázquez, Cristina; Hernández, Erwin; Ferragut, Luis; González-Aguilera, Diego; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Wind speed forecasts obtained by Numerical Weather Prediction models are limited for fine interpretation in heterogeneous terrain, in which different roughnesses and orographies occur. This limitation is derived from the use of low-resolution and grid-box averaged data. In this paper a dynamical downscaling method is presented to increase the local accuracy of wind speed forecasts. The proposed method divides the wind speed forecasting into two steps. In the first one, the mesoscale model WRF (Weather Research and Forecasting) is used for getting wind speed forecasts at specific points of the study domain. On a second stage, these values are used for feeding the HDWind microscale model. HDWind is a local model that provides both a high-resolution wind field that covers the entire study domain and values of wind speed and direction at very located points. As an example of use of the proposed method, we calculate a high-resolution wind field in an urban-interface area from Badajoz, a South-West Spanish city located near the Portugal border. The results obtained are compared with the values read by a weathervane tower of the Spanish State Meteorological Agency (AEMET) in order to prove that the microscale model improves the forecasts obtained by the mesoscale model.
Open Access
Introducing the Temporal Distortion Index to perform a bidimensional analysis of renewable energy forecast
(Elsevier, 2015-11-21) Frías Paredes, Laura; Mallor Giménez, Fermín; León, Teresa; Gastón Romeo, Martín; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa; Institute of Smart Cities - ISC
Wind has been the largest contributor to the growth of renewal energy during the early 21st century. However, the natural uncertainty that arises in assessing the wind resource implies the occurrence of wind power forecasting errors which perform a considerable role in the impacts and costs in the wind energy integration and its commercialization. The main goal of this paper is to provide a deeper insight in the analysis of timing errors which leads to the proposal of a new methodology for its control and measure. A new methodology, based on Dynamic TimeWarping, is proposed to be considered in the estimation of accuracy as attribute of forecast quality. A new dissimilarity measure, the Temporal Distortion Index, among time series is introduced to complement the traditional verication measures found in the literature. Furthermore we provide a bi-criteria perspective to the problem of comparing different forecasts. The methodology is illustrated with several examples including a real case.
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.
Embargo
EOLO, a wind energy forecaster based on public information and automatic learning for the Spanish Electricity Markets
(Elsevier, 2024-05-31) Prieto-Herráez, Diego; Martínez-Lastras, Saray; Frías Paredes, Laura; Asensio, María Isabel; González-Aguilera, Diego; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
For the correct operation of the electricity system, producers must provide an estimate of the energy they are going to discharge into the system, and they must face financial penalties if their forecasts are wrong. This is especially difficult in the case of renewable energies, and in particular wind energy because of its variability and intermittency. The tool proposed allows, in a first step, to improve the prediction of wind energy to be produced and, in a second step, to optimize the offer to be presented to the electricity market, so that the overall economic performance can be improved. This tool is based on the use of public information and automatic learning and has been evaluated on a set of 30 wind farms in Spain, using their historical production data. The results indicate improvements in both the accuracy of the energy estimation and the profit obtained from the energy sold.