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Modelo reducido de predicción de demanda de edificios residenciales en base a parámetros meteorológicos
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Orientador(es)
Resumo(s)
RESUMEN: En el proyecto Girter se desarrolló una herramienta inteligente de control de redes de distrito de calor y frío que gestione de forma conjunta generación y demanda. En este trabajo se busca anticipar la evolución de la demanda, lo que permitirá optimizar la producción, maximizando el aporte de energía renovable. Para ello se obtendrá una ecuación de orden reducido que caracterice el comportamiento energético del edificio en base a sus variables constructivas. Esta formulación matemática permite predecir, de forma estimada, la climatización de un edificio dentro de un horizonte de predicción climático disponible. Analizando los valores de energía obtenidos de una batería de simulaciones dinámicas, se escoge un modelo de calidad probada y se obtienen los parámetros correspondientes a 6 tipos de edificios. Estos permiten definir la predicción de demanda de un distrito completo.
ABSTRACT: In Girter project we developed an intelligent tool to control district heating and cooling networks, managing jointly generation and demand. The aim of this work is to anticipate the evolution of demand, which will optimize production and maximize the contribution of renewable energy. To this end, we will obtain a reduced order equation characterizing the energy behavior of the building based on its construction variables. This mathematical formulation allows to predict an estimation of the HVAC energy of a building within an available climate prediction time horizon. After analyzing the energy values obtained from a battery of dynamic simulations, we chose model of proven quality, and the associated parameters corresponding to 6 types of building are obtained. These allow a whole district demand prediction to be defined.
ABSTRACT: In Girter project we developed an intelligent tool to control district heating and cooling networks, managing jointly generation and demand. The aim of this work is to anticipate the evolution of demand, which will optimize production and maximize the contribution of renewable energy. To this end, we will obtain a reduced order equation characterizing the energy behavior of the building based on its construction variables. This mathematical formulation allows to predict an estimation of the HVAC energy of a building within an available climate prediction time horizon. After analyzing the energy values obtained from a battery of dynamic simulations, we chose model of proven quality, and the associated parameters corresponding to 6 types of building are obtained. These allow a whole district demand prediction to be defined.
Descrição
CIES2020 - XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia Solar
Palavras-chave
Residential buildings Energy efficiency District networks Renewable energies
Contexto Educativo
Citação
Díaz, J.A... [et.al.] - Modelo reducido de predicción de demanda de edificios residenciales en base a parámetros meteorológicos. In: CIES2020: As Energias Renováveis na Transição Energética: Livro de Comunicações do XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia Solar. Helder Gonçalves, Manuel Romero (Ed.). Lisboa, Portugal: LNEG, 3-5 Novembro, 2020, p. 1061-1068
Editora
LNEG - Laboratório Nacional de Energia e Geologia