An Advanced Methodology to Enhance Energy Efficiency and Performance in a Hospital Cooling-Water System

An Advanced Methodology to Enhance Energy Efficiency and Performance in a Hospital Cooling-Water System

Hospitals are a type of building with especially high energy demands; and this is owing to the fact that they run life-saving services 24 hours a day, 365 days a year. Moreover, the healthcare services offered by hospitals are growing in number and complexity, which means that their energy demands...

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Autor principal: Dulce Chamorro, Eduardo
Otros Autores: Martínez de Pisón Ascacíbar, Francisco Javier (Universidad de La Rioja)
Formato: text (thesis)
Lenguaje:eng
Publicado: Universidad de La Rioja (España) 2021
Acceso en línea:https://dialnet.unirioja.es/servlet/oaites?codigo=291498
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description Hospitals are a type of building with especially high energy demands; and this is owing to the fact that they run life-saving services 24 hours a day, 365 days a year. Moreover, the healthcare services offered by hospitals are growing in number and complexity, which means that their energy demands increase every year. In order to cover the energy needs of all this activity, a vast amount of technical installations are required. In addition, supplying energy and liquids increasingly necessitates greater control, precision, and quality. Due to the critical role of cooling-water systems, this thesis focuses on these installations that are vital for both the comfort they provide through airconditioning and for healthcare activities. The objective of this research is to improve the performance of hospital refrigeration plants to increase energy efficiency, while also reducing inefficiencies in generator start-ups and maintenance, which are commonplace problems in this type of facility. By applying Machine Learning (ML) models to predict cooling demand, it has been possible to anticipate, adapt, and plan for actual thermal generation to meet, but not exceed, expected demand. To obtain said models, an already existing methodology based on genetic algorithms called GAparsimony was utilized. This methodology allows parsimonious models to be obtained in an automated fashion. The algorithms used include artificial neural networks (ANN), support vector machines for regression (SVR), and extreme gradient boosting machines (XGBoost). Prior to the modeling phase, an extensive general optimization of the cooling-water facilities was carried out; and during this process a methodology was developed to be applied in the following areas: the control system, the data acquisition system, and the physical systems. The optimization culminated with a demand prediction model being implemented in the BMS (Building Management Systems). This feature enabled the BMS to anticipate generator programming a day in advance, thus exercising predictive management. The research presented herein has been corroborated by the results obtained when the optimization methodology was applied, and by implementing the demand prediction model in the BMS as well.
author2 Martínez de Pisón Ascacíbar, Francisco Javier (Universidad de La Rioja)
author_facet Martínez de Pisón Ascacíbar, Francisco Javier (Universidad de La Rioja)
Dulce Chamorro, Eduardo
format text (thesis)
author Dulce Chamorro, Eduardo
spellingShingle Dulce Chamorro, Eduardo
An Advanced Methodology to Enhance Energy Efficiency and Performance in a Hospital Cooling-Water System
author_sort Dulce Chamorro, Eduardo
title An Advanced Methodology to Enhance Energy Efficiency and Performance in a Hospital Cooling-Water System
title_short An Advanced Methodology to Enhance Energy Efficiency and Performance in a Hospital Cooling-Water System
title_full An Advanced Methodology to Enhance Energy Efficiency and Performance in a Hospital Cooling-Water System
title_fullStr An Advanced Methodology to Enhance Energy Efficiency and Performance in a Hospital Cooling-Water System
title_full_unstemmed An Advanced Methodology to Enhance Energy Efficiency and Performance in a Hospital Cooling-Water System
title_sort advanced methodology to enhance energy efficiency and performance in a hospital cooling-water system
publisher Universidad de La Rioja (España)
publishDate 2021
url https://dialnet.unirioja.es/servlet/oaites?codigo=291498
work_keys_str_mv AT dulcechamorroeduardo anadvancedmethodologytoenhanceenergyefficiencyandperformanceinahospitalcoolingwatersystem
AT dulcechamorroeduardo advancedmethodologytoenhanceenergyefficiencyandperformanceinahospitalcoolingwatersystem
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spelling oai-TES00000230072021-10-05An Advanced Methodology to Enhance Energy Efficiency and Performance in a Hospital Cooling-Water SystemDulce Chamorro, EduardoHospitals are a type of building with especially high energy demands; and this is owing to the fact that they run life-saving services 24 hours a day, 365 days a year. Moreover, the healthcare services offered by hospitals are growing in number and complexity, which means that their energy demands increase every year. In order to cover the energy needs of all this activity, a vast amount of technical installations are required. In addition, supplying energy and liquids increasingly necessitates greater control, precision, and quality. Due to the critical role of cooling-water systems, this thesis focuses on these installations that are vital for both the comfort they provide through airconditioning and for healthcare activities. The objective of this research is to improve the performance of hospital refrigeration plants to increase energy efficiency, while also reducing inefficiencies in generator start-ups and maintenance, which are commonplace problems in this type of facility. By applying Machine Learning (ML) models to predict cooling demand, it has been possible to anticipate, adapt, and plan for actual thermal generation to meet, but not exceed, expected demand. To obtain said models, an already existing methodology based on genetic algorithms called GAparsimony was utilized. This methodology allows parsimonious models to be obtained in an automated fashion. The algorithms used include artificial neural networks (ANN), support vector machines for regression (SVR), and extreme gradient boosting machines (XGBoost). Prior to the modeling phase, an extensive general optimization of the cooling-water facilities was carried out; and during this process a methodology was developed to be applied in the following areas: the control system, the data acquisition system, and the physical systems. The optimization culminated with a demand prediction model being implemented in the BMS (Building Management Systems). This feature enabled the BMS to anticipate generator programming a day in advance, thus exercising predictive management. The research presented herein has been corroborated by the results obtained when the optimization methodology was applied, and by implementing the demand prediction model in the BMS as well.Los hospitales son edificios que tienen una gran demanda energética debido a que en su interior albergan servicios vitales las 24 horas del día, los 365 días del año. Así mismo, las carteras de servicios de procesos asistenciales que proporcionan los hospitales son cada vez más complejos y numerosos lo que hace incrementar anualmente esta demanda. Para dar cobertura a cada una de las actividades se requiere un gran número de instalaciones técnicas. Además, el suministro de energía y de fluidos cada vez requiere mayor control, precisión y calidad. Esta tesis se ha centrado por su importancia crítica, en la instalación de generación de agua refrigerada que se requiere tanto para confort en los sistemas de aire acondicionado, como en los procesos asistenciales. El objetivo de este trabajo de investigación consiste en mejorar el funcionamiento de las plantas de refigeración de los hospitales para aumentar la eficiencia energética, así mismo para reducir las ineficencias en los arranques de los generadores y en el mantenimiento, problemas comunes en este tipo de instalaciones. Mediante el uso de modelos desarrollados mediante Machine learning (ML) aplicados en la predicción de la demanda de refrigeración, se ha conseguido anticipar, adaptar y planificar la generación térmica real a la demanda prevista. Para la obtención de los mismos se ha utilizado una metodología existente basada en algoritmos genéticos denominada GAparsimony. Esta metodología permite de una manera automatizada obtener modelos parsimoniosos. Entre los algoritmos utilizados se encuentran las artificial neural networks (ANN), las suport vector machines for regression (SVR) y las extreme gradient boosting machines (XGBoost). Previamente, se realizó una extensa optimización general de las instalaciones de generación de agua refrigerada, desarrollándose una metodología de trabajo que se aplica en los siguientes ámbitos: el sistema de control; el sistema de adquisición de datos; y en los sistemas físicos. El proceso culminó con la implantación dentro del BMS (Building Management Systems) del modelo de predicción de demanda, lo que permite anticipar un día antes la programación de los generadores necesarios, realizándose así un control predictivo. Este trabajo queda respaldado satisfactoriamente por los datos de los resultados reales obtenidos por la aplicación de la metodología de optimización, así como por la implementación en el BMS del modelo de predicción de demanda durante la duración del estudio.Universidad de La Rioja (España)Martínez de Pisón Ascacíbar, Francisco Javier (Universidad de La Rioja)2021text (thesis)application/pdfhttps://dialnet.unirioja.es/servlet/oaites?codigo=291498engLICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. 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Any link to this document should be made using its official URL in Dialnet. More info: https://dialnet.unirioja.es/info/derechosOAI

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