Abstract
This paper provides the data used in a research project to propose a new simplified windows rating system based on saved annual energy (“Developing an empirical predictive energy-rating model for windows by using Artificial Neural Network” (Shakouri Hassanabadi and Banihashemi Namini, 2012) [1], “Climatic, parametric and non-parametric analysis of energy performance of double-glazed windows in different climates” (Banihashemi et al., 2015) [2]). A full factorial simulation study was conducted to evaluate the performance of 26 different types of windows in a four-story residential building. In order to generalize the results, the selected windows were tested in four climates of cold, tropical, temperate, and hot and arid; and four different main orientations of North, West, South and East. The accompanied datasets include the annual saved cooling and heating energy in different climates and orientations by using the selected windows. Moreover, a complete dataset is provided that includes the specifications of 26 windows, climate data, month, and orientation of the window. This dataset can be used to make predictive models for energy efficiency assessment of double glazed windows.
Highlights
This paper provides the data used in a research project to propose a new simplified windows rating system based on saved annual energy (“Developing an empirical predictive energy-rating model for windows by using Artificial Neural Network” (Shakouri Hassanabadi and Banihashemi Namini, 2012) [1], “Climatic, parametric and non-parametric analysis of energy performance of doubleglazed windows in different climates” (Banihashemi et al, 2015) [2])
Presenting a comprehensive dataset monthly and annual energy performance of double-glazed windows faced to different orientations and located in diverse climates
Simulated annual energy for each window is provided in the Supplementary materials (AnnualEnergy.xlsx)
Summary
Mahmoud Shakouri a,n, Saeed Banihashemi b a School of Civil and Construction Engineering, Oregon State University, 220 Owen Hall, Corvallis, OR 97331, United States b School of Built Environment, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia article info. The accompanied datasets include the annual saved cooling and heating energy in different climates and orientations by using the selected windows. A complete dataset is provided that includes the specifications of 26 windows, climate data, month, and orientation of the window. This dataset can be used to make predictive models for energy efficiency assessment of double glazed windows.
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