{"id":{"repo_id":"burgos","oai_identifier":"oai:riubu.ubu.es:10259/7797"},"canonical_url":"https://search.dev.ndltd.org/etd/burgos/oai:riubu.ubu.es:10259/7797","repository":{"repo_id":"burgos","name":"Universidad de Burgos","base_url":"https://riubu.ubu.es/oai/request"},"display":{"title":"Daylight modeling for energy efficiency and visual comfort in buildings","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Granados López, Diego"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Alonso Tristán, Cristina","Diez Mediavilla, Montserrat"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022","date_published":"2022","updated_at":"2026-07-24T01:27:09Z","subjects":["CIE standard sky classification","Illuminance","Daylight","Meteorological indices","Machine learning","Clasificación de cielos estándar CIE","Iluminancia","Iluminación natural","Índices meteorológicos","Inteligencia artificial"],"languages":["eng"],"rights":["Attribution-NonCommercial-NoDerivatives 4.0 Internacional"],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.36443/10259/7797"],"render_values":[{"text":"10.36443/10259/7797","href":"https://doi.org/10.36443/10259/7797","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10259/7797","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Alonso Tristán, Cristina","Diez Mediavilla, Montserrat"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Universidad de Burgos. 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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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Daylight modeling for energy efficiency and visual comfort in buildings"]}]}],"canonical_facts":{"dc:contributor.advisor":["Alonso Tristán, Cristina","Diez Mediavilla, Montserrat"],"dc:contributor.other":["Universidad de Burgos. Departamento de Ingeniería Electromecánica"],"dc:creator":["Granados López, Diego"],"dc:date.accessioned":["2023-09-05T12:37:02Z"],"dc:date.available":["2023-09-05T12:37:02Z"],"dc:date.issued":["2022"],"dc:description.abstract":["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."],"dc:format.mimetype":["application/pdf"],"dc:identifier.doi":["10.36443/10259/7797"],"dc:identifier.uri":["http://hdl.handle.net/10259/7797"],"dc:language.iso":["eng"],"dc:rights":["Attribution-NonCommercial-NoDerivatives 4.0 Internacional"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["CIE standard sky classification","Illuminance","Daylight","Meteorological indices","Machine learning","Clasificación de cielos estándar CIE","Iluminancia","Iluminación natural","Índices meteorológicos","Inteligencia artificial"],"dc:title":["Daylight modeling for energy efficiency and visual comfort in buildings"],"dc:type":["info:eu-repo/semantics/doctoralThesis"]},"updated_at":"2026-07-24T01:27:09Z"}