Statistical Analysis of Window Impacts on Cooling and Heating Energy Use in Single Family Residence Based on Climate Regions in U.S.A.

Abstract

Purpose : Window area, location, and selection of glazing are a very important factor in reducing cooling and heating energy use of a building. The primary goal of this research is to investigate the effects of window design variables on annual cooling and heating energy use in a single residence based on climate regions in the United States using statistical analysis. Method : The methodologies used in this paper are building energy simulations, descriptive statistics, t-test, Latin Hypercube Sampling, and sensitivity analysis. Two groups of window variables are defined and simulated to explore the difference of the simulation results using Latin Hypercube Sampling and t-test. Then, the enter method as a regression model is used to investigate which group of data better predicted annual cooling and heating energy use. Lastly, Standard Regression Coefficients (SRCs) sensitivity indicator is used to determine if the influence of window parameters on cooling and heating energy use varies by different climate zone. Results : T-test results show that the differences in simulation results between the two groups are not statistically significant. That means that simulations using less number of variables (Group B) can have similar accuracy than simulation with higher number of window variables (Group A). As a result of the regression models, average adjusted R2 is 0.886 for Group A and 0.933 for Group B. Therefore, the regression model using Group B is selected to determine the effect of each variable on energy use. According to SRCs from regression, the most sensitive design parameters for cooling energy use are SHGC, west and south facing windows, while U-value, north and west facing windows are the most sensitive window parameters for heating energy use.