Open Access
Sampling design optimization of ground radiometric stations
(Springer Nature, 2019-01-23) Martín-Pomares, Luisa; Gastón Romeo, Martín; Polo, Jesús; Frías Paredes, Laura; Fernández-Peruchena, Carlos; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
This chapter presents a methodology to identify optimal site locations to establish a surface radiometric monitoring network once the raw solar estimations are produced from satellite images or numerical models. The site selection is done considering the long-term solar resource, its spatial distribution, variability and technical and logistics aspects. The methodology presented here is an adaptive sampling strategy under an assumed population model derived from satellite images or numerical models. The objective is to install the radiometric stations in optimal locations to correct the systematic biases of the modelled solar radiation, improving the estimates and minimizing the number of stations needed. To achieve that, we need to identify the area with a similar dynamic in terms of solar radiation. Inside the areas identified, the most optimal locations will be used to place the radiometric stations. The methodology is divided into three phases. The first phase divides the geographical extension under study to identify the areas with a similar dynamic in terms of monthly solar radiation. The selection of the number of cluster/areas is done with an information criteria technique. Once the optimal number of clusters and the extension of each area is defined, the second phase is the production of a long list of candidate sampling sites with GIS techniques based on constraints to identify the best locations in each area to place the radiometric stations. The third phase is based on ranking the long list of candidate locations with site visits and a checklist criterion based on BSRN recommendations to produce a final shortlist of optimal sites to measure solar radiation in each area. Finally, two tiers of radio-metric stations are proposed to place in each area depending on the ranking level of each location.
Open Access
Outcomes and features of the inspection of receiver tubes (ITR) system for improved O&M in parabolic trough plants
(American Institute of Physics, 2018) Olano, Xabier; García de Jalón, Alberto; Pérez, David; García Barberena, Javier; López, Javier; Gastón Romeo, Martín; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
Concentrated solar power (CSP) plants based on parabolic trough (PT), after several years since their commissioning, demand new operation and maintenance (O&M) developments. Particularly, the receiver tube´s potential degradation over time is a real challenge. In this paper, the current version of the ITR System's last developments and advanced features are presented together with the main outcomes provided by the system for a real, complete solar field inspection in a commercial PT power plant. Exemplarily, this commercial ITR inspection showed that 0.8% of the tubes were underperforming and thus classified as outliers, while the average relative power of the tubes from the solar field resulted in about 97% of the ideal tubes' power. This paper shows that, thanks to the ITR Inspection System, plant operators can more easily develop and adopt improved O&M strategies, such as corrective and preventive actions in the solar field and even predictive actions in case of periodic inspections.
Open Access
Dynamic mean absolute error as new measure for assessing forecasting errors
(Elsevier, 2018-02-14) Frías Paredes, Laura; Mallor Giménez, Fermín; Gastón Romeo, Martín; León, Teresa; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Accurate wind power forecast is essential for grid integration, system planning, and electricity trading in certain electricity markets. Therefore, analyzing prediction errors is a critical task that allows a comparison of prediction models and the selection of the most suitable model. In this work, the temporal error and absolute magnitude error are simultaneously considered to assess the forecast error. The trade-off between both types of errors is computed, analyzed, and interpreted. Moreover, a new index, the dynamic mean absolute error, DMAE, is defined to measure the prediction accuracy. This index accounts for both error components: temporal and absolute. Real cases of wind energy forecasting are used to illustrate the use of the new DMAE index and show the advantages of this new index over other error indices.
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
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
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
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
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
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
Analysis of the suitability of the EOLO wind-predictor model for the spanish electricity markets
(MDPI, 2023) Martínez-Lastras, Saray; Frías Paredes, Laura; Prieto-Herráez, Diego; Gastón Romeo, Martín; González-Aguilera, Diego; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Wind energy forecasting is a critical aspect for wind energy producers, given that the chaotic nature and the intermittence of meteorological wind cause difficulties for both the integration and the commercialization of wind-produced electricity. For most European producers, the quality of the forecast also affects their financial outcomes since it is necessary to include the impact of imbalance penalties due to the regularization in balancing markets. To help wind farm owners in the elaboration of offers for electricity markets, the EOLO predictor model can be used. This tool combines different sources of data, such as meteorological forecasts, electric market information, and historic production of the wind farm, to generate an estimation of the energy to be produced, which maximizes its financial performance by minimizing the imbalance penalties. This research study aimed to evaluate the performance of the EOLO predictor model when it is applied to the different Spanish electricity markets, focusing on the statistical analysis of its results. Results show how the wind energy forecast generated by EOLO anticipates real electricity generation with high accuracy and stability, providing a reduced forecast error when it is used to participate in successive sessions of the Spanish electricity market. The obtained error, in terms of RMAE, ranges from 8%, when it is applied to the Day-ahead market, to 6%, when it is applied to the last intraday market. In financial terms, the prediction achieves a financial performance near 99% once imbalance penalties have been discounted.

Gastón Romeo, Martín

Email Address

person.page.identifierURI

Birth Date

Job Title

Last Name

First Name

person.page.departamento

person.page.instituteName

ORCID

person.page.observainves

person.page.upna

Name

Filters

Author

Subject

Date

Has files

Item Type

Type

Settings

Sort By

Results per page

Search Results