Artículo

Methodological approach for the development of a simplified residential building energy estimation in temperate climate

Reus Netto, Gabriela Icon; Mercader Moyano, María del Pilar ; Czajkowski, Jorge Daniel Icon

Otros responsables

Fecha

08/2019

Editorial

Molecular Diversity Preservation International

Revista

Sustainability (Switzerland)

Título del libro

Nombre del evento

Fecha del evento

Institución organizadora

ISSN

2071-1050

eISSN

ISBN

Idioma

Inglés

Tipo del evento

Clasificación temática

Resumen

Energy ratings and minimum requirements for thermal envelopes and heating and air conditioning systems emerged as tools to minimize energy consumption and greenhouse gas emissions, improve energy efficiency and promote greater transparency with regard to energy use in buildings. In Latin America, not all countries have building energy efficiency regulations, many of them are voluntary and more than 80% of the existing initiatives are simplified methods and are centered in energy demand analysis and the compliance of admissible values for different indicators. However, the application of these tools, even when simplified, is reduced. The main objective is the development of a simplified calculation method for the estimation of the energy consumption of multifamily housing buildings. To do this, an energy model was created based on the real use and occupation of a reference building, its thermal envelope and its thermal system's performance. This model was simulated for 42 locations, characterized by their climatic conditions, whilst also considering the thermal transmittance fulfilment. The correlation between energy consumption and the climatic conditions is the base of the proposed method. The input data are seven climatic characteristics. Due to the sociocultural context of Latin America, the proposed method is estimated to have more possible acceptance and applications than other more complex methods, increasing the rate of buildings with an energy assessment. The results have demonstrated a high reliability in the prediction of the statistical models created, as the determination coefficient (R2) is nearly 1 for cooling and heating consumption.

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