https://hdl.handle.net/10259/6232 2026-04-20T09:06:04Z 2026-04-20T09:06:04Z Guillán Lorenzo, Omar Suárez García, Andrés González Peña, David García Fuente, Manuel Granados López, Diego https://hdl.handle.net/10259/7270 2024-05-14T10:39:48Z 2022-09-01T00:00:00Z

A Low-Cost Luxometer Benchmark for Solar Illuminance Measurement System Based on the Internet of Things Guillán Lorenzo, Omar; Suárez García, Andrés; González Peña, David; García Fuente, Manuel; Granados López, Diego 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.

2022-09-01T00:00:00Z García Rodríguez, Ana García Rodríguez, Sol Granados López, Diego Diez Mediavilla, Montserrat Alonso Tristán, Cristina https://hdl.handle.net/10259/7237 2024-05-14T10:09:05Z 2022-03-01T00:00:00Z

Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones García Rodríguez, Ana; García Rodríguez, Sol; Granados López, Diego; Diez Mediavilla, Montserrat; Alonso Tristán, Cristina 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.

2022-03-01T00:00:00Z Granados López, Diego García Rodríguez, Ana García Rodríguez, Sol Suárez García, Andrés Diez Mediavilla, Montserrat Alonso Tristán, Cristina https://hdl.handle.net/10259/6322 2024-05-14T11:29:23Z 2021-12-01T00:00:00Z

Pixel-Based Image Processing for CIE Standard Sky Classification through ANN Granados López, Diego; García Rodríguez, Ana; García Rodríguez, Sol; Suárez García, Andrés; Diez Mediavilla, Montserrat; Alonso Tristán, Cristina 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.

2021-12-01T00:00:00Z Sáiz Manzanares, María Consuelo Queiruga Dios, Miguel Ángel García Osorio, César Montero García, Eduardo Rodríguez Medina, Jairo https://hdl.handle.net/10259/6265 2021-12-10T09:19:43Z 2019-11-01T00:00:00Z

Observation of Metacognitive Skills in Natural Environments: A Longitudinal Study With Mixed Methods Sáiz Manzanares, María Consuelo; Queiruga Dios, Miguel Ángel; García Osorio, César; Montero García, Eduardo; Rodríguez Medina, Jairo Recent studies pointing to evaluation methods in natural environments suggest that their use in the analysis of metacognitive skills provides more precise information than the use of off-line evaluation methods. In this research, mixed methods are used over one academic year for the evaluation of the metacognitive skills that students of Secondary Education apply to solve physics problems. The objectives of this study are to analyze the use of metacognitive skills in natural environments and to study behavioral patterns of student learning through a longitudinal study. A total of 509 recordings of think-aloud protocols are analyzed through the categorization of the responses (liquefying) and the protocol of Van der Stel and Veenman for the analysis of the quality of metacognitive skills. Fewer conceptual errors and less uncertainty over vocabulary were noted during the academic year. Nevertheless, a degree of ambiguity persisted in the understanding of physics concepts. The metacognitive skills of Orientation and Planning were used more than any others. The technique of graph analysis is also applied, to establish the patterns of behavior of each student throughout the academic year. Different patterns were found, the analysis of which helped to identify academically challenged and at-risk students. The use of mixed observation techniques and graph analysis facilitated information on the pace of learning of each student. Future studies will be directed at proposals for the automation of these evaluation techniques in natural learning environments.

2019-11-01T00:00:00Z