Synthesizing test Reference Year files from known climate patterns of nearby cities
Author(s): |
José Aderson Araújo Passos Filho
Bruno de Payva y. Raviolo Natasha Catunda Nayana Helena Barbosa de Castro Karoline Cordeiro de Andrade Daniel Ribeiro Cardoso |
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Medium: | journal article |
Language(s): | Portuguese |
Published in: | PARC Pesquisa em Arquitetura e Construção, December 2019, v. 10 |
Page(s): | e019030 |
DOI: | 10.20396/parc.v10i0.8653706 |
Abstract: |
The importance of an architecture adapted to its climatic context is often debated. In order to avoid future unexpected environmental behavior or failure of a building during its use, building simulation tools are used in the design and require complete and consistent weather data. However, such data are not always available for the locations where buildings are simulated, and the use of data from neighboring cities becomes usual. There are, though, several uncertainties involved in the behavior of environmental variables when the climate of large urban centers is attributed to nearby localities and areas with more significant vegetation cover, water bodies, different topography, among others. The present paper aims to present the process of preparing a weather file for the Pecém Industrial and Port Complex, located at 40 km from the capital Fortaleza, Brazil, in order to be used in simulations during the design process of buildings. The synthesis of the file was achieved through the collection and treatment of information measured in loco, the application of recommended models for the estimation of missing data, and the development of an alternative method for the estimation of a Test Reference Year of localities without weather data of several years. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
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data sheet - Reference-ID
10411822 - Published on:
12/02/2020 - Last updated on:
02/06/2021