Búsqueda y evaluación de emplazamientos óptimos para albergar instalaciones de energías renovables en la costa de la Región de Murcia: combinación de Sistemas de Información Geográfica (SIG) y Soft Computing

[SPA] Las instalaciones de energías renovables (EERR), impulsadas inicialmente por un marco legislativo favorable, por la necesidad de disminuir la dependencia energética de combustibles fósiles y, posteriormente por una evolución progresiva extraordinaria, han experimentado una creciente expansión y desarrollo en el sector energético español en las últimas décadas. La búsqueda y selección de emplazamientos óptimos para implantar instalaciones de EERR, no sólo requiere disponer de un grupo de asesoramiento capaz de evaluar y analizar las diferentes alternativas, sino que resulta conveniente aplicar un conjunto de herramientas y métodos que faciliten el proceso de tomas de decisiones. El problema de localización de instalaciones de energías renovables es un problema complejo, dado que se necesitan manejar gran variedad de criterios de distinta índole y procedencia, así como evaluar en la mayoría de los casos un gran número de alternativas. Para ello, los métodos de decisión multicriterio constituyen la metodología principal para abordar este problema combinándolos por un lado con los Sistemas de Información Geográfica que, nos servirán de soporte de base de datos a nuestra problema y por otro con las herramientas de Soft Computing aplicadas en las propias metodologías de decisión que, les confieren a éstas el modelado de la incertidumbre y la vaguedad de los datos que se manejan en este tipo de problemas. En particular y como ejemplo de aplicación, en la presente investigación se propone la combinación de distintas metodologías de decisión multicriterio con Sistemas de Información Geográfica como soporte de base de datos, y el Soft Computing y en concreto la lógica difusa con el objetivo de evaluar y clasificar los emplazamientos óptimos de instalaciones de EERR en la costa de la Región de Murcia.[ENG] Renewable energy facilities, initially driven by a favourable legislative framework due to the need to reduce energy dependence on fossil fuels and later by a progressive extraordinary evolution, have experienced an increasing expansion and development in the Spanish energy sector in recent decades. The search and selection of optimal locations to implant renewable energy facilities, requires not only to have an advisory group able to assess and analyze alternatives, but it is appropriate to apply a set of tools and methods to facilitate the decision making process. The locating problem of renewable energy facilities is a complex problem because it needs to handle a great variety of criteria with different nature and origin, as well as to evaluate, in most cases, a large number of alternatives. For this, multicriteria decision methods are the main methods to tackle this problem by combining them, on the one hand with Geographic Information Systems that will be used as support database to our problem, and on the other hand, with Soft Computing tools applied in the own decision methodologies which give them the uncertainty modeling and vagueness of the data used in this type of problems. In particular and as an example of application, in this investigation the combination of multicriteria decision methods with Geographic Information Systems is proposed as support database, and Soft Computing (specifically the Fuzzy Logic) for the search and evaluation of optimal locations to implant renewable energy facilities on the coast of the Region of Murcia.Universidad Politécnica de Cartagen

Low-Cost Surface Classification System Supported by Deep Neural Models

Determining the surface on which a vehicle is moving is vital information for im-proving active safety systems. Performing the surface classification or estimating adherence through tire slippage can lead to late action in possible risk situations. Currently, approaches based on image, sound, or vibration analysis are emerging as a viable alternative, though sometimes complex. This work proposes a methodology based on the use of low-cost accelerometers combined with Deep Learning tech-niques. The performance of the proposed system is evaluated with real tests, where high percentages of accuracy are obtained in the classification task.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Identification and selection of potential sites for onshore wind farms development in Region of Murcia, Spain

It is often advisable to combine spatial representation tools such as Geographic Information Systems (GIS) with Multi criteria Decision Making Methods (MCDM) when solving location complex problems. The current case refers to the search for and selection of sites for onshore wind farms on the coast of the Region of Murcia, in the southeast of Spain. When resolving the proposed problem, the legal restrictions and the criteria (wind speed, area, slope, etc.) that influence the location will be considered. These will be defined in the form of thematic layers that will be entered into the GIS. Restrictions will be imposed taking into account the legislative framework of the study area so that, through their analysis and editing, it will be possible to reduce the initial area and obtain suitable sites where this type of facilities can be installed. Moreover, as the objective of the study is to select the locations and obtain a ranking two different models will be applied, initially a categorical assessment through a lexicographic order will be performed using the tools available in the GIS and, later it will be applied the ELECTRE-TRI methodology will be applied in order to make a comparison between the methods.This work is partially supported by FEDER funds, the DGICYT and Junta de Andalucía under projects TIN2011-27696-C02-01 and P11-TIC-8001, respectively

Spatio-temporal dynamic clustering modeling for solar irradiance resource assessment

Nowadays, with the development of international policies and agreements to promote the integration of renewable energy sources, mainly solar and wind, modeling the solar resource by including the spatio-temporal variability is crucial to determine future PV power plant locations and estimate potential power generation performances. However, contributions involving long-term periods and different time windows to explore such potential solar resource variability are generally scarce. Under this framework, the present paper proposes a methodology focused on characterizing and clustering the spatio-temporal solar resource variability through the global horizontal irradiance analysis. Hierarchical clustering technique is firstly used to classify the spatial data. Different time windows — from short-term to long-term data — can be subsequently evaluated by using various sources of information. The Spanish territory is selected as case study, considering 22-year period data (1999–2020) and 1,936,917 observations from online satellite database. Spatial variability and geographical clustering differences are discussed and compared depending on the selected time windows, identifying relevant spatial variations for some specific months. Additionally, some years present more variability as well, in line with the sunspot peak of the solar cycles. The proposed approach gives an alternative comprehensive spatio-temporal clustering and characterization of GHI evolution, providing a suitable methodology to help the current European sustainable energy transition.These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program, United States. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. This work was partially funded by the research project PID2020–112754GB–I00, financially supported by the Ministerio de Ciencia e Innovación (Spain)

Multidimensional analysis of groundwater pumping for irrigation purposes: economic, energy and environmental characterization for PV power plant integration

Nowadays, the agriculture sector presents relevant opportunities to integrate renewable energy sourcesas an alternative solution to mitigate fossil-fuel dependence and decrease emissions. Moreover, thissector demands a detailed review of energy uses and other factors that are addressed as priority issues inmost developed countries. In this framework, groundwater pumping energy requirements for agricultureirrigation emerge as a relevant topic to be improved in terms of power demand. Actually, this demand iscurrently supplied by diesel equipment solutions, with relevant drawbacks such as:ðiÞa large energydependence on fossil fuels for the agricultural sector andðiiÞa lack of participation in reducing CO2emissions.This paper proposes a multidimensional characterization to evaluate photovoltaic (PV) solar energyintegration into groundwater pumping requirements. Alternative solutions are compared under eco-nomic, energy and environmental aspects; thus providing an extensive scenario where the considerableinfluence of multiple factors such as water needs, irrigation area or aquifer depth are explicitlyconsidered. Extensive results based on a real Spanish aquifer and discussion about the solutions are alsoincluded in the paper.This work is partially supported by projects Ref. TIN2014-55024-P from the Spanish Ministry of Science and Innovation (including FEDER funds), and SENECA Foundation 19882-GERM-15

MCDM-based multidimensional approach for selection of optimal groundwater pumping systems: design and case example

Herein, optimal groundwater pumping solutions based on a variety of energy resources and water storage options are estimated and classified. Each energy source and water storage option is first characterized considering energy, economic, and environmental criteria. A multi-criteria decision making (MCDM) process based on the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) is subsequently applied to identify and classify the optimal groundwater pumping solutions under such a multidimensional framework. An aquifer located in the southeast of Spain is analyzed in a case study to assess the proposed optimal MCDM-based approach. Conventional diesel-based equipment, solar PV power plants, and direct grid connection, as well as three water storage systemseedirect pumping, seasonal storage, and annual storageeeare identified as potential energy sources and water storage options, respectively. Characterization and visualization of these energy and water storage systems, as well as prioritized option results, are also discussed herein.This work is partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO), reference TIN2017-86647-P. The authors also acknowledge the support of the Fundación Séneca (Region of Murcia, Spain) through the Grant 19882-GERM-15

Net-Metering and Self-Consumption Analysis for Direct PV Groundwater Pumping in Agriculture: A Spanish Case Study

International policies mainly that are focused on energy-dependence reduction and climate change objectives have been widely proposed by most developed countries over the last years. These actions aim to promote the integration of renewables and the reduction of emissions in all sectors. Among the different sectors, agriculture emerges as a remarkable opportunity to integrate these proposals. Indeed, this sector accounts for 10% of the total greenhouse gas (GHG) emissions in the EU, representing 1.5% of gross domestic product (GDP) in 2016. Within the agriculture sector, current solutions for groundwater pumping purposes are mainly based on diesel technologies, leading to a remarkable fossil fuel dependence and emissions that must be reduced to fulfill both energy and environmental requirements. Relevant actions must be proposed that are focused on sustainable strategies and initiatives. Under this scenario, the integration of photovoltaic (PV) power plants into groundwater pumping installations has recently been considered as a suitable solution. However, this approach requires a more extended analysis, including different risks and impacts related to sustainability from the economic and energy points of view, and by considering other relevant aspects such as environmental consequences. In addition, PV solar power systems connected to the grid for groundwater pumping purposes provide a relevant opportunity to optimize the power supplied by these installations in terms of self-consumption and net-metering advantages. Actually, the excess PV power might be injected to the grid, with potential profits and benefits for the agriculture sector. Under this scenario, the present paper gives a multidimensional analysis of PV solar power systems connected to the grid for groundwater pumping solutions, including net-metering conditions and benefit estimations that are focused on a Spanish case study. Extensive results based on a real aquifer (Aquifer 23) located in Castilla La Mancha (Spain) are included and discussed in detail.This research was funded by the research project TIN2017-86647-P from the Spanish Ministry of Science and Innovation (including FEDER funds), and the Seneca Foundation 19882-GERM-15

GIS based solar resource analysis for irrigation purposes: Rural areas comparison under groundwater scarcity conditions

During the past decade, most governments have been promoting energy efficiency programs and the integration of renewable energy sources into the majority of energy uses. Among the different sectors, the agricultural sector is becoming a more active participant to reduce fossil fuel dependence and improve environmental sustainability. Indeed, agriculture usually combines both a high energy demand and water problems associated with over-exploited aquifers, providing great potential and remarkable opportunities to change the energy mix and maximize the use and integration of natural resources in a rational way. Considering this framework, the present paper describes and compares the solar resource integration and its effects on agricultural pumping purposes in two Mediterranean countries, Spain and Morocco, with significant differences in terms of energy mix, climatic conditions and energy policies. As a novel contribution in this paper, we propose the use of GIS to analyze the spatial and temporal variability of the solar resource through real data of both locations, as well as to study groundwater resources. With this aim, two technical proposals for irrigation purposes are compared in terms of environmental benefits, CO2 emissions and agriculture energy model changes: diesel equipment and photovoltaic system. Results based on solar radiation resource, pumping requirements and aquifer depth are included in the paper.The authors appreciate the EU Marenostrum-Erasmus Mundus Program, which allows us to collaborate with the Moulay Ismail University in Meknes (Morocco). This work has been partially supported by funds, DGICYT and Junta de Andalucía under projects TIN 2014-55024-P and P11-TIC-8001 respectively, and Seneca Foundation 19882-GERM-15

Análisis y estimación de superficie basada en Mapas Auto-Organizados

En este trabajo se propone la utilización de Mapas Auto-Organizados para llevar a cabo la tarea de clasificación de superficies y estimación de adherencia. Este tipo de redes neuronales se caracteriza por emplear el paradigma del Aprendizaje No Supervisado, logrando un aprendizaje autónomo de las características de los datos que le permitan elaborar la separación de los datos. La información de partida sobre la cual se desarrolla este trabajo es la vibración producida por la rodadura del neumático en distintas superficies. El análisis previo de los datos permite la extracción de características estadísticas sobre las que el SOM zealizará su trabajo. Éstos mapas agrupan conjuntos similares de datos en zonas próximas y además generan una reducción dimensional del problema al mostrarse sobre un plano bidimensional. Este hecho, facilita el análisis de problemas con numerosas variables de entrada al poder trabajarse de manera visual y sencilla. Además, permite la validación de los datos para su uso directo o para ser empleados en otros sistemas en etapas posteriores, así como la inferencia de nueva información.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Análisis del procesamiento de imágenes médicas pulmonares para el diagnóstico y tratamiento del SDRA

El síndrome de dificultad de respiración agudo (SDRA), es una afección pulmonar que dificulta la capacidad de respirar del paciente que lo padece, actualmente se busca apoyo de la tecnología para poder dar un diagnostico preciso. Este soporte se ha buscado entorno al procesamiento de imágenes de los pulmones, de los pacientes con el fin de conocer el estado en el que se encuentran, actualmente existen demasiadas herramientas en e, mercado que pueden ayudar con dicho procesamiento, pero no siempre se obtienen los mejores resultados. El objetivo de este proyecto es generar una herramienta modular que permita tanto adherir como eliminar algoritmos de segmentación de imágenes médicas, y así mantener una herramienta dinámica que genere resultados útiles para el apoyo del tratamiento del SDRA.Fhe Acute Respiratory Distress Syndrome (ARDS) is a dangerous syndrome that affects the lungs of a person, making breathing difficult or impossible. This syndrome needs be handled very carefully and for that reasorí doctors looks for technological support, looking for tools and programs that help them with the diagnostic and treatment. Currently there exist many tools that require time to use them effectively and in many cases, those tools don't present the expected results, making the doctor waste his time. The objective of this project is to build a modular tool capable of integrating image segmentation algorithms that helps in executlng a better and more clear segmentation process over the medical images and bring a better support for the ARDS treatmentIngeniero (a) de SistemasPregrad