A New Analytical Approach for Evaluating Fenestration Impacts on Residential Building Energy Performance

Two-parameterlinear regression is used in this study to understand the interaction of windowU-Factor and solar heat gain (SHGC) on building energy performance. The (3)coefficients from the regression analysis are identified with the followingnomenclature: “b”. This scalar represents energy attributable to theopaque portion of the building envelope. The scalar itself is fixed (nofunctionality to the windows) but does change with building type and location.“m1”. This scalar is a multiplier for the window U-Factor and representsthe change in building energy for the total window area versus the opaque wallit displaces. “m2”. Scalar for window solar heat gain. When the value ispositive (+) window solar hear gain increases building energy. A negative (-)scalar value indicates that passive gains offset more heating load thencooling. The regression equation to describe building energy as a function ofwindow U & SHGC takes the form: Y = b + m1*[U-Factor]+ m2*[SHGC] where Y = total (energy, dollars, carbon, etc.) In thisstudy there are four primary findings: 1.With the near ubiquitous use of air-conditioning across NorthAmerica, increased fan sizes to deliver air-conditioningcan negatively affectheating energy performance. Potential passive solar benefits are offset when alarger furnace fan isrequired for cooling loads.2.Extrapolating beyond market “reasonable” on U-Factor for trade-offanalyses biases the cold climate analysis in favor ofpassive solar gains. Theexample here would be including single pane glass in a cold climate regressionwhenreplacement products are more typically at or slightly better than code(e.g. low-E double pane).3.The U vs. SHGC trade-offs suggested by the U.S. and Canada ENERGY STARprograms do not properly address orientation. Both programs use equal windowdistribution for all 4 sides of the building to predict average window energyperformance. This shorthand approach fails to properly account for the impactsof solar exposure differences across the seasons. 4.Windowenergy performance is surprisingly consistent across a wide range ofresidentialbuilding insulation vintages and HVAC fuel sources. With this knowledge windowenergy savings for the entire market, not just new or existing buildings, areeasier to predict.