BUSCADOR RECOLECTA

Spain is a country with a high dependence on fossil fuels. For this reason, in 2007, it implemented a bonus system that aimed to encourage the production of renewable energies, particularly photovoltaic solar energy. These production bonuses, guaranteed by the Spanish government, led to an exponential increase in the number of companies in the market and, consequently, the MWh produced. However, in 2012, given the excessive budgetary burden involved in maintaining this “feed-in tariff” system and after several years of institutional instability, the aforementioned system of incentives for phoyovoltaic (PV) energy was eliminated. This paper has tried to analyze the consequences of this institutional change, a clear example of the “hold up” problem. For this purpose, a sample of 5354 companies, which was divided, geographically, into Spanish regions and, temporarily, into three different periods, has been taken, considering diverse economic and financial variables. The results show a notable weakening of the sector that, due to the effects of the regulatory change, has lost attractiveness and profitability for investors and is consequently suffering from stagnation, which has led to the disappearance of many companies in the sector., Xunta de Galicia | Ref. ED431C2018/48, Xunta de Galicia | Ref. ED431E2018/07, Ministerio de Economía y Competitividad | Ref. RTI2018-099225-B-100, Junta de Castilla y León | Ref. ORDEN EDU/667/2019, Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-098900-B-I00

The construction of hydropower plants often requires the flooding of large land areas, causing considerable alterations in the natural environment. In the region surrounding the reservoir of the Corumbá IV hydroelectric plant, located in the Cerrado region of Central Brazil, two types of soil predominate, classified as Dystroferric Red Latosol and Dystroferric Haplic Cambisol. The plant owners have to restore the degraded biome after the flooding of the margins caused by the filling of the reservoir. An experiment was carried out with fifteen native species, selected for having ideal phytosociological properties. Nine of them showed a survivability considered satisfactory in a planting situation, with a view to large-scale planting. Assuming that the planting of native fruit trees can be a quick solution to the attraction and preservation of wildlife, it would therefore provide sustainable riparian revegetation around the reservoir. We adopted the SIMOS technique to rank the criteria based on four morphological features and a Fuzzy AHP model to rank the contributions of the nine fruit tree species to the sustainable restoration of part of the riparian vegetation cover around the reservoir. In practical terms, we concluded that the soil types did not have any influence on tree survival after two years of growth, but the native trees’ morphological features varied among the species. These findings simplify the large-scale planting of seedlings that must be carried out by the operator in the riparian forest around the reservoir., Corumbá Concessões S/A | Ref. PD-2262-1204/2012, Ministerio de Ciencia e Innovación | Ref. RTI2018-098900-B-I00, Junta de Castilla y León | Ref. INVESTUN/19/BU/0004

Daylight availability knowledge is the first step for an energetic and visually efficient building and city design. It can be estimated with the Vertical Sky Component (VSC), which is defined as the ratio of the vertical diffuse illuminance over the unobstructed horizontal diffuse illuminance, simultaneously measured at the same point. These illuminance magnitudes are obtained from luxmeter measurements but these data are scarce. Alternatively, VSC can be obtained from prior knowledge of the sky illuminance distribution, which can be measured with a sky scanner device or by reference to the CIE (Commission Internationale de L’Éclairage) Standard classification for homogeneous skies. Both approaches are compared in this study. The coherence of the results obtained for the four cardinal orientations are analyzed by applying classical statistical parameters and luxmeter measurements as references for the results. The measurement campaign was completed between September 2016 and January 2019 in Burgos (Spain), as representative case study and specific contribution of this work. It was observed that the VSC values were higher than 100 in many cases: 21.94% for the south- and 33.6% for the east-facing vertical surfaces. The study highlights the good daylighting conditions in Burgos, mainly due to the predominance of clear skies over much of the year. This fact implies high daylight availability that, with efficient city planning and building design, could potentially lead reduction energy consumption of buildings, improvements in visual comfort, and the well-being of occupants., Junta de Castilla y León | Ref. EDU / 667/2019, Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-098900-B-I00, Junta de Castilla y León | Ref. EDU / 556/2019

In this study, ten-minute meteorological data-sets recorded at Burgos, Spain, are used to develop models of Photosynthetic Active Radiation () following two different procedures: multilinear regression and Artificial Neural Networks. Ten Meteorological Indices (MIs) are chosen as inputs to the models: clearness index (), diffuse fraction (), direct fraction (), Perez's clear sky index (ɛ), brightness index (), cloud cover (), air temperature (), pressure (), solar azimuth cosine (), and horizontal global irradiation (). The experimental data are clustered according to the sky conditions, following the CIE standard sky classification. A previous feature selection procedure established the most adequate MIs for modelling in clear, partial and overcast sky conditions. was the common MI used by all models and for all sky conditions. Additional variables were also included: the geometrical parameter, , and three variables related to the sky conditions, , and Both modelling methods, multilinear regression and ANN, yielded very high determination coefficients () with very close results in the models for each of the different sky conditions. Slight improvements can be observed in the ANN models. The results underline the equivalence of multilinear regression models and ANN models of PAR following previous feature selection procedures., Regional Government of Castilla y León, under projects BU021G19 and INVESTUN/19/BU/0004 and the Spanish Ministry of Science & Innovation under the I+D +i state program “Challenges Research Projects” (Ref. RTI2018-098900-B-I00). Diego Granados López expresses his thanks to the Junta de Castilla y León for economic support (PIRTU Program, ORDEN EDU/556/2019).

Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image-processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty-two image-processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scanner data. Color spaces, spectral features, and texture filters image-processing methods are applied. While the use of the traditional RGB color space for image-processing yielded good results (ANN accuracy equal to 86.6%), other color spaces, such as Hue Saturation Value (HSV), which may be more appropriate, increased the accuracy of their global classifications. The use of either the green or the blue monochromatic channels improved sky classification, both for the fifteen CIE standard sky types and for simpler classification into clear, partial, and overcast conditions. The main conclusion was that specific image-processing methods could improve ANN-algorithm accuracy, depending on the image information required for the classification problem., Regional Government of Castilla y León under the “Support Program for Recognized Research Groups of Public Universities of Castilla y León” (BU021G19) and the Spanish Ministry 595 of Science and Innovation under the I + D + i state program “Challenges Research Projects” (Ref. RTI2018-098900-B-I00). Diego Granados López expresses his thanks for economic support from the Junta de Castilla-León (PIRTU Program, ORDEN EDU/556/2019).

Concern about the harmful effects that ultraviolet (UV) rays have on the skin of people who are routinely exposed to solar radiation has driven the industry of skin protection creams, sunglasses and clothing. Spanish Navy personnel are subject to different levels of exposure depending on their rank and function. The objective of this research is to analyze the behavioral variables associated to the effects on the skin caused by UV rays, denoted by the combined effects of perceived susceptibility and perceived severity, on their decision to purchase and wear uniforms with UV protection. A confirmatory analysis using a structural equation modeling (SEM) was performed on a sample of 100 respondents. The model results revealed a strong mediating characteristic of the intention to use, variable associated with the exogenous variables. Attitude towards the use of clothing and social influence, as well as the exogenous variable clothing action planning, on the sun protective clothing use during tactical maneuvers. These relationships were significant with p-values close to zero. However, exogenous variables related to perceived susceptibility and perceived severity in exposure to sunlight did not represent a significant influence when mediated by self-efficacy in use. The results revealed the consequence of awareness about the importance of protecting oneself and the influence that usage habits can have on the military with respect to the decision to purchase uniforms with UV protection., The authors gratefully acknowledge the financial support provided by the Spanish Ministry of Science & Innovation under the I+D+i state program “Challenges Research Projects” (RTI2018-098900-B-I00) and the Regional Government of Castilla y León (INVESTUN/19/BU/004 and INVESTUN/22/BU/0001).

Four models for predicting Photosynthetically Active Radiation (PAR) were obtained through MultiLinear Regression (MLR) and an Artificial Neural Network (ANN) based on 10 meteorological indices previously selected from a feature selection algorithm. One model was developed for all sky conditions and the other three for clear, partial, and overcast skies, using a sky classification based on the clearness index (kt). The experimental data were recorded in Burgos (Spain) at ten-minute intervals over 23 months between 2019 and 2021. Fits above 0.97 and Root Mean Square Error (RMSE) values below 7.5% were observed. The models developed for clear and overcast sky conditions yielded better results. Application of the models to the seven experimental ground stations that constitute the Surface Radiation Budget Network (SURFRAD) located in different Köppen climatic zones of the USA yielded fitted values higher than 0.98 and RMSE values less than 11% in all cases regardless of the sky type., This research was funded by the Spanish Ministry of Science and Innovation, grant number RTI2018-098900-B-I00, and Consejería de Empleo e Industria, Junta de Castilla y León, grant number INVESTUN/19/BU-0004.

Thermal comfort is essential when wearing a postural-corrective garment. Discomfort of any kind may deter regular use and prolong user recovery time. The objective of this work is therefore to optimize a new compound that can alter the temperature of orthopedic insoles, thereby improving the thermal comfort for the user. Its novelty is a resin composite that contains a thermoregulatory Phase-Change Material (PCM). An experimental design was used to optimize the proportions of PCM, epoxy resin, and thickener in the composite and its effects. A Box–Behnken factor design was applied to each compound to establish the optimal proportions of all three substances. The dependent variables were the Shore A and D hardness tests and thermogravimetric heat-exchange measurements. As was foreseeable, the influence of the PCM on the thermal absorption levels of the compound was quantifiable and could be determined from the results of the factor design. Likewise, compound hardness was determined by resin type and resin-PCM interactions, so the quantity of PCM also had some influence on the mechanical properties of the composite. Both the durability and the flexibility of the final product complied with current standards for orthopedic insoles., The authors gratefully acknowledge the financial support provided by the Spanish Ministry of Science and Innovation under the I+D+i state program “Challenges Research Projects” (RTI2018-098900-B-I00).

In this study, ten-minute meteorological data-sets recorded at Burgos, Spain, are used to develop models of
Photosynthetic Active Radiation (PAR) following two different procedures: multilinear regression and Artificial
Neural Networks. Ten Meteorological Indices (MIs) are chosen as inputs to the models: clearness index (kt),
diffuse fraction (kd), direct fraction (kb), Perez’s clear sky index (ε), brightness index (Δ), cloud cover (CC), air
temperature (T), pressure (P), solar azimuth cosine (cosZ), and horizontal global irradiation (RaGH). The
experimental data are clustered according to the sky conditions, following the CIE standard sky classification. A
previous feature selection procedure established the most adequate MIs for modelling PAR in clear, partial and
overcast sky conditions. RaGH was the common MI used by all models and for all sky conditions. Additional
variables were also included: the geometrical parameter, cosZ, and three variables related to the sky conditions,
kt, ε, and Δ. Both modelling methods, multilinear regression and ANN, yielded very high determination coefficients (R2) with very close results in the models for each of the different sky conditions. Slight improvements
can be observed in the ANN models. The results underline the equivalence of multilinear regression models and
ANN models of PAR following previous feature selection procedures., The authors gratefully acknowledge the financial support provided by the Regional Government of Castilla y Leon, ´ under projects BU021G19 and INVESTUN/19/BU/0004 and the Spanish Ministry of Science & Innovation under the I+D +i state program “Challenges Research Projects” (Ref. RTI2018-098900-B-I00). Diego Granados Lopez ´ expresses his thanks to the Junta de Castilla y Leon ´ for economic support (PIRTU Program, ORDEN EDU/556/2019).

Natural illumination has an important place in home automation applications. Among
other advantages, it contributes to better visual health, energy savings, and lower CO2 emissions.
Therefore, it is important to measure illuminance in the most accurate and cost-effective way. This
work compares several low-cost commercial sensors (VEML 7700, TSL2591, and OPT3001) with a
professional one (ML-020S-O), all of them installed outdoors. In addition, a platform based on the
Internet of Things technology was designed and deployed as a centralized point of data collection and
processing. Summer months have been chosen for the comparison. This is the most adverse situation
for low-cost sensors since they are designed for indoor use, and their operating range is lower than
the maximum reached by sunlight. The solar illuminance was recorded every minute. As expected,
the obtained bias depends on the solar height. This can reach 60% in the worst circumstances,
although most of the time, its value stays below 40%. The positive side lies in the good precision of
the recordings. This systematic deviation makes it susceptible to mathematical correction. Therefore,
the incorporation of more sensors and data that can help the global improvement of the precision
and accuracy of this low-cost system is left as a future line of improvement., The authors gratefully acknowledge the financial support provided by the Spanish Ministry of Science & Innovation under the I+D+i state program “Challenges Research Projects” (RTI2018-098900-B-I00) and the Regional Government of Castilla y León (IN-VESTUN/19/BU/004 and INVESTUN/22/BU/0001). Diego Granados López also thankfully acknowledges the financial support from the Junta de Castilla-León (ORDEN EDU/556/2019).

The construction of hydropower plants often requires the flooding of large land areas,
causing considerable alterations in the natural environment. In the region surrounding the reservoir
of the Corumbá IV hydroelectric plant, located in the Cerrado region of Central Brazil, two types of
soil predominate, classified as Dystroferric Red Latosol and Dystroferric Haplic Cambisol. The plant
owners have to restore the degraded biome after the flooding of the margins caused by the filling
of the reservoir. An experiment was carried out with fifteen native species, selected for having
ideal phytosociological properties. Nine of them showed a survivability considered satisfactory in a
planting situation, with a view to large-scale planting. Assuming that the planting of native fruit
trees can be a quick solution to the attraction and preservation of wildlife, it would therefore provide
sustainable riparian revegetation around the reservoir. We adopted the SIMOS technique to rank
the criteria based on four morphological features and a Fuzzy AHP model to rank the contributions
of the nine fruit tree species to the sustainable restoration of part of the riparian vegetation cover
around the reservoir. In practical terms, we concluded that the soil types did not have any influence
on tree survival after two years of growth, but the native trees’ morphological features varied among
the species. These findings simplify the large-scale planting of seedlings that must be carried out by
the operator in the riparian forest around the reservoir., This research was funded by Corumbá Concessões S/A, grant number PD-2262-1204/2012, Spanish Ministry of Science and Innovation, grant number RTI2018-098900-B-I00, and the Regional Government of Castilla y León under the “Health and Safety Program” (INVESTUN/19/BU/0004).

The melting process of lauric acid in a square container heated from the top surface was
numerically studied from an experimental case. Knowledge of this process is of special interest
for computationally efficient modeling systems, such as PCM-enhanced photovoltaic panels in
horizontal positions or energy storage using PCM embedded on flat surfaces. In these systems, the
geometric arrangement of the PCM hinders the fluid-phase movements through natural convection,
which slows the melting process and can cause overheating in the fluid phase. Using Ansys Fluent
Software, three different approaches and two simulation methods, enthalpy-porosity and effective
heat capacity, were developed for the numerical study. The results were compared with experimental
measurements in a successful evaluation of the accuracy of computational fluid dynamics simulations.
It could be observed that the effective heat capacity method presented significant advantages over the
enthalpy-porosity method, since similar accuracy results were obtained, and a lower computational
cost was required., The authors gratefully acknowledge the financial support provided by the Regional Government of Castilla y León under the “Support Program for Recognized Research Groups of Public Universities of Castilla y León” (BU021G19) and the Spanish Ministry of Science & Innovation under the R+D+i state program “Challenges Research Projects” (Ref. RTI2018-098900-B-I00).

Prediction of energy production is crucial for the design and installation of PV plants. In
this study, five free and commercial software tools to predict photovoltaic energy production are
evaluated: RETScreen, Solar Advisor Model (SAM), PVGIS, PVSyst, and PV*SOL. The evaluation
involves a comparison of monthly and annually predicted data on energy supplied to the national
grid with real field data collected from three real PV plants. All the systems, located in Castile and
Leon (Spain), have three different tilting systems: fixed mounting, horizontal-axis tracking, and
dual-axis tracking. The last 12 years of operating data, from 2008 to 2020, are used in the evaluation.
Although the commercial software tools were easier to use and their installations could be described
in detail, their results were not appreciably superior. In annual global terms, the results hid poor
estimations throughout the year, where overestimations were compensated by underestimated
results. This fact was reflected in the monthly results: the software yielded overestimates during
the colder months, while the models showed better estimates during the warmer months. In most
studies, the deviation was below 10% when the annual results were analyzed. The accuracy of the
software was also reduced when the complexity of the dual-axis solar tracking systems replaced the
fixed installation., This research was funded by Spanish Ministry of Science and Innovation, grant number RTI2018-098900-B-I00 and the Regional Government of Castilla y León under the “Support Program for Recognized Research Groups of Public Universities of Castilla y León” (ORDEN EDU/667/2019) and “Health and Safety Program” (INVESTUN/19/BU/0004).

Solar Ultraviolet Radiation (UVR), which is identified as a major environmental health
hazard, is responsible for a variety of photochemical reactions with direct effects on urban and
aquatic ecosystems, human health, plant growth, and the deterioration of industrial systems. Ground
measurements of total solar UVR are scarce, with low spatial and temporal coverage around the
world, which is mainly due to measurement equipment maintenance costs and the complexities of
equipment calibration routines; however, models designed to estimate ultraviolet rays from global
radiation measurements are frequently used alternatives. In an experimental campaign in Burgos,
Spain, between September 2020 and June 2022, average values of the ratio between horizontal global
ultraviolet irradiance (GHUV) and global horizontal irradiance (GHI) were determined, based on
measurements at ten-minute intervals. Sky cloudiness was the most influential factor in the ratio,
more so than any daily, monthly, or seasonal pattern. Both the CIE standard sky classification and the
clearness index were used to characterize the cloudiness conditions of homogeneous skies. Overcast
sky types presented the highest values of the ratio, whereas the clear sky categories presented
the lowest and most dispersed values, regardless of the criteria used for sky classification. The
main conclusion, for practical purposes, was that the ratio between GHUV and GHI can be used to
model GHUV., This research was funded by the Spanish Ministry of Science and Innovation, grant numbers RTI2018-098900-B-I00 and TED2021-131563B-I00, and Junta de Castilla y León, grant numbers INVESTUN/19/BU/0004 and INVESTUN/22/BU/0001.

Photosynthetically Active Radiation (PAR) is a fundamental parameter for developing plant productivity models. Nevertheless,
instrumentation for measuring PAR and to record it is scarce at conventional meteorological stations. Several procedures have therefore
been proposed for PAR estimation. In this work, 21 previously published analytical models that correlate PAR with easily available
meteorological parameters are collected. Although longer time scales were considered in the original publications, a minute range
was applied in this work to calibrate the PAR models. In total, more than 10 million input records were gathered from the SURFRAD
station network from a 10-year long time series with data frequencies recorded every 1 min. The models were calibrated both globally,
using data from all stations and locally, with data from each station. After calibration, the models were validated for minute, hourly and
daily data, obtaining low fitting errors at the different stations in all cases, both when using the globally calibrated models and with the
models calibrated for each location. Although the PAR results in general improved for locally calibrated models, the use of local models
is not justified, since the global models presented offered very satisfactory PAR results for the different climatic conditions where the
meteorological stations are located. Thus, PAR estimation model should then be selected, solely considering the meteorological variables
available at the specific location. When applying the globally calibrated models to input data classified according to sky conditions (from
clear to overcast), the PAR models continued to perform satisfactorily, although the error statistics of some models for overcast skies
worsened., The authors gratefully acknowledge the financial support provided by the Spanish Ministry of Science & Innovation under the I + D+i state program “Challenges Research Projects” (Ref. RTI2018-098900-B-I00).

In the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (IUV), on the basis of data collected
at Burgos (Spain) during an experimental campaign between March 2020 and May 2022. The aim
is to explore the effectiveness of a range of variables for modelling horizontal ultraviolet irradiance
through a comparison of supervised artificial neural network (ANN) and regression model results.
A preliminary feature selection process using the Pearson correlation coefficient was sufficient to
determine the variables for use in the models. The following variables and their influence on horizontal ultraviolet irradiance were analyzed: horizontal global irradiance (IGH), clearness index (kt),
solar altitude angle (α), horizontal beam irradiance (IBH), diffuse fraction (D), temperature (T), sky
clearness (ε), cloud cover (Cc), horizontal diffuse irradiance (IDH), and sky brightness (∆). The ANN
models yielded results of greater accuracy than the regression models., This research is a result of the project RTI2018-098900-B-I00 financed by the Spanish Ministry of Science and Innovation, project TED2021-131563B-I00 financed by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR», and Junta de Castilla y León, under grant number INVESTUN/19/BU/0004.

Comunicación presentada en: CIES 2022 - XVIII Congreso Ibérico y XIV Congreso Iberoamericano de Energía Solar. Palma de Mallorca, 20 al 22 de junio de 2022, El aprovechamiento de la iluminación natural permite aumentar la calidad de vida y desarrollar la actividad humana. Para modelar la luminancia, la Comisión Internacional de Iluminación (CIE) propone una clasificación estándar que comprende quince clases de cielos. Sin embargo, la aplicación de este estándar requiere entradas que solo pueden obtenerse mediante costosos dispositivos. Por ello, existen multitud de modelos desarrollados para, de manera simplificada, clasificar el cielo. En particular, este estudio propone cinco categorías que permiten una clasificación más detallada que la tradicional en tres categorías simples como claro-nublado-cubierto. Además, se proporciona una alternativa basada en el aprendizaje automático utilizando índices meteorológicos como entradas. Las técnicas seleccionadas para realizar la clasificación alternativa fueron las redes neuronales y los árboles de decisión. En base a los resultados obtenidos, es posible clasificar el cielo en 5 categorías con ambas técnicas con eficacia., The use of natural lighting allows to increase the quality of life and to develop human activity. To model luminance, the International Commission on Illumination of the Sky (CIE) proposes fifteen classes of skies. Nonetheless, the application of this standard requires inputs that can only be obtained by expensive devices, so there are a numerous models developed to simplify the sky classification. In particular, this study proposes five categories that allow a more detailed classification than the traditional one: clear-cloudy-overcast. In addition, an alternative based on machine learning using meteorological indices as inputs is provided. The selected techniques were neural networks and decision trees. According to the results, it is possible to classify effectively the sky into 5 categories with both techniques., Este trabajo se ha desarrollado en el marco de los proyectos INVESTUN/22/BU/0001 de Junta De Castilla y León, Consejería de Empleo y el proyecto RTI2018-098900-B-I00 Ministerio de Ciencia, Innovación y Universidades. Por su apoyo financiero, Ignacio García agradece al Ministerio de Universidades y a la Unión Europea-Next Generation EU (Programa de recualificación del sistema universitario español 2021-2023, Resolución 1402/2021), y Diego Granados-López agradece a la Junta de Castilla y León (Programa PIRTU, ORDEN EDU/556/2019).

Comunicación presentada en: CIES 2022 - XVIII Congreso Ibérico y XIV Congreso Iberoamericano de Energía Solar. Palma de Mallorca, 20 al 22 de junio de 2022, En este trabajo se ha realizado una comparación de la radiación solar captada por un seguidor solar a dos ejes orientado a la producción de energía fotovoltaica considerando tres estrategias de seguimiento: (1) orientación del seguidor perpendicularmente a los rayos del sol, (2) posicionamiento horizontal del seguidor y (3) orientación del seguidor que maximice la captación de irradiancia en cielos no despejados atendiendo a la distribución angular de radiancia y no a la posición del sol. Para las estrategias 1 y 3, que implican la inclinación del plano y, por lo tanto, la captación de irradiancia reflejada, se han considerado cuatro valores de albedo diferentes. Esta evaluación se ha llevado a cabo a partir de un año de observaciones de irradiancia global, difusa y directa y distribución angular de radiancia y luminancia realizadas en la estación meteorológica del grupo de investigación SWIFT de la Universidad de Burgos. Los resultados han mostrado que las estrategias propuestas incrementan significativamente la radiación solar, celeste y reflejada, captada en condiciones de cielo no despejado, especialmente con valores bajos de albedo., In this work, a comparison of the solar radiation captured by a two-axis solar tracker oriented to the production of photovoltaic energy has been performed considering three tracking strategies: (1) orientation of the tracker normal to the sun's rays, (2) horizontal positioning of the tracker and (3) orientation of the tracker that maximizes the capture of irradiance in overcast skies according to the angular distribution of radiance and not to the position of the sun. For strategies 1 and 3, which involve tilting the plane and thus capturing reflected irradiance, four different albedo values have been considered. This evaluation has been carried out on the basis of one year of observations of global, diffuse and direct irradiance and angular distribution of sky radiance and luminance registered at the meteorological station of the SWIFT research group of the University of Burgos. The results have shown that the proposed alternative strategies significantly increase the solar radiation gain, both celestial and reflected, captured under overcast conditions, especially at low albedo values., Este trabajo se ha desarrollado en el marco del proyecto Análisis espectral de la radiación solar: Aplicaciones climáticas, energéticas y biológicas (RTI2018-098900-B-I00) financiado por el Ministerio de Ciencia, Innovación y Universidades dentro del programa estatal de I+D+i “Retos Investigación”. Ignacio García agradece al Ministerio de Universidades y a la Unión Europea-Next Generation EU su apoyo financiero (Programa de recualificación del sistema universitario español 2021-2023, Resolución 1402/2021). Diego Granados-López agradece a la Junta de Castilla y León su apoyo financiero (Programa PIRTU, ORDEN EDU/556/2019).

Searching and selecting an adequate methodology for daylight modeling
is essential in the design of energy efficient buildings that guarantee the
visual, physical and psychological comfort of their occupants. The first step
in determining the indoor building illuminance lies in knowing the outdoor
illuminance. This dissertation addresses this key aspect through different
strategies such as luminous efficacy models and the determination of the
angular distribution of the sky's luminance.
Daylight is strongly determined by sky conditions. The CIE/ISO standard
provides a good general framework to represent the real conditions of the
sky, covering the entire probable spectrum of skies, and has been used as
a reference throughout this work. The characterization of the skies
according to the CIE standard requires experimental measurements of the
luminance distribution of the sky, scarcely recorded in terrestrial
meteorological facilities. The thesis proposes, as alternatives for the
classification of skies according to the CIE taxonomy, the use of
meteorological indices, sky images and algorithms based on artificial
intelligence. The structure and efficiency of the machine learning
algorithms used, both neural networks and decision trees, have been
optimized through feature selection procedures in the case of the use of
meteorological indices and through image pre-processing techniques, as a
step prior to using the classification algorithm. The thesis has also
developed a new locally calibrated luminous efficacy model, with excellent
results both when used for all-sky types and for clear, overcast and
partially overcast sky conditions., La búsqueda y elección de una metodología adecuada para el modelado
de la iluminación natural es fundamental en el diseño de edificios
energéticamente eficientes y que garanticen el confort visual, físico y
psicológico de sus ocupantes. El primer paso para la determinación de la
iluminación en el interior de un edificio reside en el conocimiento de la
iluminación exterior. La tesis doctoral aborda este aspecto fundamental a
través de diferentes estrategias como son los modelos de eficacia luminosa
y la determinación de la distribución angular de la luminancia del cielo.
La iluminación natural está fuertemente determinada por las
condiciones de cielo. El estándar CIE/ISO proporciona un buen marco
general para representar las condiciones reales del cielo cubriendo todo el
espectro probable de cielos, por lo que se ha seleccionado como referencia a
lo largo de este trabajo. La caracterización de los cielos según el estándar
CIE requiere de medidas experimentales de la distribución de luminancia
del cielo, escasamente registradas en las instalaciones meteorológicas
terrestres. La tesis propone como alternativas para la clasificación de cielos
según la taxonomía CIE, la utilización de índices meteorológicos, imágenes
del cielo y algoritmos basados en inteligencia artificial. La estructura y la
eficacia de los algoritmos de aprendizaje automático empleados, redes
neuronales y árboles de decisión, se han optimizado mediante
procedimientos de selección de variables en el caso de la utilización de
índices meteorológicos y mediante técnicas de pre-procesamiento de
imágenes, como paso previo a la utilización del algoritmo de clasificación.
La tesis ha desarrollado también un nuevo modelo de eficacia luminosa,
calibrado localmente, con excelentes resultados tanto al utilizarlo para
todos los tipos de cielo como para condiciones de cielo claro, cubierto y
parcialmente cubierto., This doctoral thesis has been supported thanks to the funding of the
PROGRAMA DE FORMACIÓN DE PROFESORADO UNIVERSITARIO
(PIRTU ORDEN EDU/556/2019), the Mobility Grant for Doctoral Students
Stays of the University of Burgos, Program (2021), and the following
competitive funding research projects:
1. Análisis Espectral de la Radiación Solar: Aplicaciones Climáticas,
Energéticas y Biológicas (RTI-2018-098900-B-I00). Ministerio de
Universidades e Investigación Programa Estatal De I+D+i
Orientada a los Retos de la Sociedad. IP: Cristina Alonso Tristán y
Montserrat Díez Mediavilla. 1/01/2019-30/09/2022.
2. Valoración técnica de los niveles de exposición a radiación solar en
trabajos de exterior: identificación de grupos de riesgo y medidas
de prevención. (INVESTUN/19/BU/004) Junta de Castilla y León. Dirección General de Trabajo y Prevención de riesgos laborales. IP:
Montserrat Díez Mediavilla. 01/01/2019-30/09/2021.
3. Metodología para la rehabilitación energética de edificios de uso
público en Castilla y León mediante integración fotovoltaica
(BU021G19). Junta de castilla y León. Programa de Apoyo a los
Grupos de Investigación Reconocidos de Universidades públicas de
Castilla y León. 01/01/2019-31/12/2021. IP: Montserrat Díez
Mediavilla.
4. Medida y modelización de la iluminación solar para la optimización
de técnicas de iluminación natural en la edificación (ENE2014-
54601-R), Ministerio de Economía y Competitividad. RETOS DE
LA SOCIEDAD. IP: Montserrat Díez Mediavilla. 01/12/2015-
31/12/2018

La radiación fotosintéticamente activa (𝑃𝐴𝑅) es la componente de la
radiación solar que ejerce una mayor influencia en la fotosíntesis y el
crecimiento vegetal. La vegetación actua como sumidero de CO2,
mitigando los efectos del cambio climático, por lo que conocer la
influencia de la 𝑃𝐴𝑅 en el crecimiento vegetal es primordial. La
modelización matemática de la 𝑃𝐴𝑅 permite estimar su valor a partir de
otras variables, sin necesidad de disponer de instrumentos de medida
específicos, ya que no es habitual encontrar, en las estaciones
radiométricas, sensores que midan esta componente de la radiación
solar.
En este trabajo, se ha modelado matemáticamente la 𝑃𝐴𝑅 en Burgos,
España. Para ello, se ha analizado estadísticamente la 𝑃𝐴𝑅 en la
localidad, analizando la ratio de esta componente con la irradiancia
global horizontal. Se ha realizado una exhaustiva revisión de los
modelos existentes y se han calibrado y validado 21 de ellos con datos
experimentales procedentes de siete estaciones radiométricas
estadounidenses, pertenecientes a la red SURFRAD. La mayor parte
de los estudios publicados por otros autores, se centran en resultados
para cielos claros, limitando su aplicación al ámbito local y esas
condiciones de cielo.
Mediante procedimientos de machine learning, aplicados a los datos
experimentales obtenidos en Burgos, se ha realizado una selección de
variables para modelar la 𝑃𝐴𝑅 mediante regresiones multilineales y
redes neuronales. Estos estudios han permitido obtener modelos
matemáticos, diferentes para cada tipo de cielo (cubiertos, parciales y
claros) clasificados según la norma ISO/CIE y alternativamente,
utilizando como parámetro de clasificación el índice de claridad (𝑘𝑡). El
comportamiento de estos últimos modelos, calibrados localmente para Burgos, ha sido evaluado frente a las medidas de siete estaciones
radiométricas de la red SURFRAD, con diferente climatología,
obteniendo muy buenos resultados y permitiendo afirmar que estos
modelos pueden utilizarse en cualquier localización,
independientemente del clima., Photosynthetically active radiation (𝑃𝐴𝑅) is the component of solar
radiation that most influences photosynthesis and plant growth.
Vegetation acts as a CO2 sink, mitigating the effects of climate change.
Therefore, knowledge of the influence of 𝑃𝐴𝑅 on plant growth is of
essential importance. Mathematical modelling makes allows estimating
𝑃𝐴𝑅 from different meteorological indices, without the need for specific
measuring instruments, since it is not usual to find sensors measuring
𝑃𝐴𝑅 at radiometric stations.
In this thesis, 𝑃𝐴𝑅 has been mathematically modelled in Burgos (Spain).
For this purpose, a statistical study has been performed at this location,
analysing the ratio of 𝑃𝐴𝑅 with the global horizontal irradiance. An
exhaustive review of the existing models has been carried out. Thus, 21
of them have been calibrated and validated with experimental data from
the 7 radiometric stations belonging to the SURFRAD network (USA).
Most of the studies published by other authors focus on results for clear
skies, limiting their application to the local area and to those sky
conditions.
Using machine learning procedures, applied to the experimental data
obtained in Burgos, a selection of variables has been made to model the
𝑃𝐴𝑅 by means of multilinear regressions and neural networks. These
studies have made it possible to obtain different mathematical models
for each sky type (overcast, partial and clear) classified according to the ISO/CIE standard and, alternatively, using the clearness index (𝑘𝑡
) as a
classification parameter. The performance of the latter models, locally
fitted for Burgos, has been evaluated against the SURFRAD network
measurements obtaining very good results. Therefore, it can be stated
that these models may be used at any location, regardless of the climate., Esta tesis ha sido financiada gracias a los proyectos de investigación
siguientes:
1.- Valoración técnica de los niveles de exposición a radiación solar en
trabajos de exterior: identificación de grupos de riesgo y medidas de
prevención. (INVESTUN/19/BU/004) Junta de Castilla y León. Dirección
General de Trabajo y Prevención de riesgos laborales. IP: Montserrat
Díez Mediavilla. 01/01/2019-30/09/2021.
2.- Análisis Espectral de la Radiación Solar: Aplicaciones Climáticas,
Energéticas y Biológicas (RTI-2018-098900-B-I00). Ministerio de
Universidades e Investigación Programa Estatal De I+D+i Orientada a
los Retos de la Sociedad. IP: Cristina Alonso Tristán y Montserrat Díez
Mediavilla. 1/01/2019-30/09/2022.
3.- Metodología para la rehabilitación energética de edificios de uso
público en Castilla y León mediante integración fotovoltaica
(BU021G19). Junta de Castilla y León. Programa de Apoyo a los
Grupos de Investigación Reconocidos de Universidades públicas de
Castilla y León. 01/01/2019-31/12/2021. IP: Montserrat Díez Mediavilla

La radiación ultravioleta es la región del espectro solar cuya longitud de onda está comprendida entre 100 y 400 nm. La importancia del conocimiento de esta radiación es debida a la gran influencia que tiene en distintos aspectos de la salud y la vida en la tierra. A pesar de la importancia de esta banda del espectro solar, muy pocas estaciones meteorológicas poseen sensores para su medición, por lo que es esencial poder determinar su valor a partir de otras variables que se miden de forma más habitual en estaciones terrestres. En este trabajo se ha realizado un estudio matemático completo de esta banda del espectro solar en sus componentes global y eritemática ambas sobre el plano horizontal, utilizando diferentes estrategias que combinan modelos de regresiones multilineales tradicionales, y novedosas técnicas de aprendizaje automático basadas en redes neuronales artificiales. También se ha abordado una aplicación práctica de los modelos de ecuaciones estructurales, que ha permitido la obtención de información latente en datos cualitativos procedentes de encuestas en un grupo de control. Los estudios realizados han permitido obtener modelos locales precisos de radiación UV y UV eritemática en función de las condiciones atmosféricas, determinadas según el tipo de cielo (todo tipo de cielo, cubierto, intermedio y claro) clasificados mediante la norma ISO/CIE. Además, se ha realizado un estudio, mediante modelos de ecuaciones estructurales, de la influencia de la percepción subjetiva de las personas al riesgo que supone para la salud la radiación UV, y su repercusión en hábitos de comportamiento, como es el uso de ropa de protección en sus actividades al aire libre., Ultraviolet irradiation is the region of the solar spectrum with wavelengths ranging from 100 to 400nm. Understanding ultraviolet irradiation is crucial due to its significant impact on various aspects of health and life on Earth. Despite the importance of this band of the solar spectrum, very few meteorological stations are equipped with sensors to measure it. Therefore, it is essential to determine its value from other variables that are more commonly measured at ground stations. In this study, a comprehensive mathematical analysis solar ultraviolet irradiation, including its global and erythemal components on the horizontal plane, was conducted using various strategies that combine traditional multiple linear regression models and innovative machine learning techniques based on artificial neural networks. Additionally, a practical application of structural equation models was employed to extract latent information from qualitative data obtained through surveys in a control group. The conducted studies have allowed for the development of accurate local models for UV irradiation and erythemal UV irradiation based on atmospheric conditions, categorized according to the type of sky (all sky types, overcast, intermediate, and clear) as defined by the ISO/CIE standard. Furthermore, through structural equation models, an analysis was conducted on the influence of people's subjective perception of the health risks associated with UV irradiation and its impact on behavioural habits, such as the use of protective clothing during outdoor activities., Esta tesis ha sido financiada en convocatorias competitivas, gracias a los siguientes proyectos de investigación: 1.- Valoración técnica de los niveles de exposición a radiación solar en trabajos de exterior: identificación de grupos de riesgo y medidas de prevención. (INVESTUN/19/BU/004). Junta de Castilla y León. Dirección General de Trabajo y Prevención de riesgos laborales. IP: Montserrat Díez Mediavilla. 01/01/2019-31/12/2021. 2.- Análisis Espectral de la Radiación Solar: Aplicaciones Climáticas, Energéticas y Biológicas (RTI-2018-098900-B-I00). Ministerio de Universidades e Investigación Programa Estatal De I+D+i Orientada a los Retos de la Sociedad. IP: Cristina Alonso Tristán y Montserrat Díez Mediavilla. 1/01/2019-30/09/2022. 3.- Modelado espectral de la radiación solar en entornos urbanos: una oportunidad para la sostenibilidad de las ciudades. (TED2021-131563B-I00). Agencia Estatal de Investigación. IP: Cristina Alonso Tristán. 1/12/2022-30/11/2024. 4.- Valoración técnica de los niveles óptimos de iluminación efectiva para la salud visual y psicológica en entornos laborales. (INVESTUN/22/BU/001). Junta de Castilla y León. Dirección General de Trabajo y Prevención de riesgos laborales. IP: Cristina Alonso Tristán. 1/01/2022-30/09/2024. 5.- Avances para un urbanismo de bajo consumo energético. (PID2022139477OB-I00). Agencia Estatal de Investigación. IP: Cristina Alonso Tristán y David González Peña. 1/09/2023-31/08/2026

Trabajo presentado a la XII National and y III International Conference on Engineering Thermodynamics. Madrid, 2022, Agriculture is one of the most important economic activities in the Castilla y León region (Spain),
approximately one-third of the land area is destined for this use. The role of agriculture in rural areas
is essential from a territorial perspective, where rural depopulation is originating a crisis in the
countryside. Moreover, climate change is having a major impact on agricultural productivity as a
consequence, among others, of the increase in the frequency and the severity of extreme events,
especially in Mediterranean regions [1]. For these reasons, it is essential to highlight the vision of
agriculture as a multifunctional activity. Besides good management, productivity depends on many
other factors, such as soil properties or climatic factors. A decisive climatic variable, which is often
not considered in the planning and management of the crops, is the Photosynthetically Active
Radiation (PAR), specifically, the Intercepted Photosynthetically Active Radiation (IPAR). The main purpose of this study is to calculate and represent an estimation of IPAR values for the most characteristic crops of Castilla y León., The authors gratefully acknowledge the financial support provided by the Spanish Ministry of Science
& Innovation under the I+D+i state program Challenges Research Projects (Ref. RTI2018-098900-
B-I00). Elena Garrachón-Gómez thanks financial support provided by Junta de Castilla y León
(Programa Operativo de Empleo Juvenil, Fondo Social Europeo e Iniciativa de Empleo Juvenil).

Photosynthetically Active Radiation (PAR) is a fundamental parameter for developing plant productivity models. Nevertheless,
instrumentation for measuring PAR and to record it is scarce at conventional meteorological stations. Several procedures have therefore
been proposed for PAR estimation. In this work, 21 previously published analytical models that correlate PAR with easily available
meteorological parameters are collected. Although longer time scales were considered in the original publications, a minute range
was applied in this work to calibrate the PAR models. In total, more than 10 million input records were gathered from the SURFRAD
station network from a 10-year long time series with data frequencies recorded every 1 min. The models were calibrated both globally,
using data from all stations and locally, with data from each station. After calibration, the models were validated for minute, hourly and
daily data, obtaining low fitting errors at the different stations in all cases, both when using the globally calibrated models and with the
models calibrated for each location. Although the PAR results in general improved for locally calibrated models, the use of local models
is not justified, since the global models presented offered very satisfactory PAR results for the different climatic conditions where the
meteorological stations are located. Thus, PAR estimation model should then be selected, solely considering the meteorological variables
available at the specific location. When applying the globally calibrated models to input data classified according to sky conditions (from
clear to overcast), the PAR models continued to perform satisfactorily, although the error statistics of some models for overcast skies
worsened., The authors gratefully acknowledge the financial support provided by the Spanish Ministry of Science & Innovation under the I+D+i state program ‘‘Challenges Research Projects” (Ref. RTI2018-098900-B-I00). Open access funding provided by Universidad Pública de Navarra.

Prediction of energy production is crucial for the design and installation of PV plants. In this study, five free and commercial software tools to predict photovoltaic energy production are evaluated: RETScreen, Solar Advisor Model (SAM), PVGIS, PVSyst, and PV*SOL. The evaluation involves a comparison of monthly and annually predicted data on energy supplied to the national grid with real field data collected from three real PV plants. All the systems, located in Castile and Leon (Spain), have three different tilting systems: fixed mounting, horizontal-axis tracking, and dual-axis tracking. The last 12 years of operating data, from 2008 to 2020, are used in the evaluation. Although the commercial software tools were easier to use and their installations could be described in detail, their results were not appreciably superior. In annual global terms, the results hid poor estimations throughout the year, where overestimations were compensated by underestimated results. This fact was reflected in the monthly results: the software yielded overestimates during the colder months, while the models showed better estimates during the warmer months. In most studies, the deviation was below 10% when the annual results were analyzed. The accuracy of the software was also reduced when the complexity of the dual-axis solar tracking systems replaced the fixed installation., This research was funded by Spanish Ministry of Science and Innovation, grant number RTI2018-098900-B-I00 and the Regional Government of Castilla y León under the 'Support Program for Recognized Research Groups of Public Universities of Castilla y León' (ORDEN EDU/667/2019) and 'Health and Safety Program' (INVESTUN/19/BU/0004).