2016-11-02T18:49:00

Heating and cooling energy lost throughwindows in the residential sector (estimatedat two-thirdsof the energy lost through windows in ali sectors) Currently accountsfor 3 percent(or 2.8 quads) of total US energyuse, costing over $26 billionannuallyin enerj_ybills. Installationof energy-efficientwindows is actingto reducethe amountof energy lost per unit window area. Installationof more energy efficientwindows since 1970 has resultedin an annualsavings of approximately0.6 quads. If ali windows utilized existingcost effective energy conserving technologies, then residential window energylosses would amountto less tlaan0.8 quads, directly saving $18 billionperyear in avoided energycosts. The nationwideinstallationof windows that are now being developed could actuallyturnthis energyloss into a net energygain. Considering only naturalreplacementof windows and new construction,appropriatefenestrationpolicies could helprealizethis potentialby reducingannualresidentialwindow energylosses to 2.2 quads by the year 2012, despitea growinghousingstock. This paper describesan analytictool developed to estimate window energy use and the potential for advancedwindow technologiesto save energy. It combineshighly disaggregated data on existing and projectedwindow stocks, buildingthermalintegrities,and heating,ventilation and air conditioning (HVAC) equipmentefficiencieson a consistent basis to produceregional ' estimates of window energy losses for a variety of window technologies. We use the tool to estimatethe contribution of energylosses from residentialwindows to total US energy use. We also estimate the annual savings which are resultingfrom the adoption of more energy efficient windows since 1970. Finally,we speculate on the potential energy savings that could resultfrom greateradoption of currentlyavailable advanced window technologies in the residentialsector. The commercialsector, although having one third of the national window stock, has differentenergyneeds, requiting a differentphysical model and is thereforenot modeled in this paper. The tool which we have developed addressesa middleground between forecasting models and technicalpotential studies. We rely on the rich data structureof end use forecastingmodels to assemble a consistent framework for assessing the impacts of window energy loss on the basis of location, building type, fuel, and HVAC equipment type. We go beyond traditionalend use forecasting models by furthercharacterizingthe energyuse consequences of various fenestration technologies on residentialheating and cooling loads with the use of a bu;.2ding energy simulation model. This detailed, technology-based description is traditionallyconsidered by technical potential studies. However,whereas technical potential studiesoRen suppress detailsof market dynamics, we relyon forecasting data for the turnoverof housing stock and technology diffusion to estimatean explicit rate of adoption for window technologies. We describe this tool and its application in the five sections following this introduction. Window ...