The air leakage of a building envelope can be determined from fan pressurization measurements with a blower door. More than 70,000 air leakage measurements have been compiled into a database. In addition to air leakage, the database includes other important characteristics of the dwellings tested, such as floor area, year built, and location. There are also data for some houses on the presence of heating ducts, and floor/basement construction type.The purpose of this work is to identify house characteristics that can be used to predict air leakage. We found that the distribution of leakage normalized with floor area of the house is roughly lognormal. Year built and floor area are the two most significant factors to consider when predicting air leakage: older and smaller houses tend to have higher normalized leakage areas compared to newer and larger ones. Results from multiple linear regression of normalized leakage with respect to these two factors are presented for three types of houses: low-income, energy-efficient, and conventional. We demonstrate a method of using the regression model in conjunction with housing characteristics published by the US Census Bureau to derive a distribution that describes the air leakage of the single-family detached housing stock. Comparison of our estimates with published datasets of air exchange rates suggests that the regression model generates accurate estimates of air leakage distribution.

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▪ Abstract  This paper explores how long-term energy forecasts are created and why they are useful. It focuses on forecasts of energy use in the United States for the year 2000 but considers only long-term predictions, i.e., those covering two or more decades. The motivation is current interest in global warming forecasts, some of which run beyond a century. The basic observation is that forecasters in the 1950–1980 period underestimated the importance of unmodeled surprises. A key example is the failure to foresee the ability of the United States economy to respond to the oil embargos of the 1970s by increasing efficiency. Not only were most forecasts of that period systematically high, but forecasters systematically underestimated uncertainties. Long-term energy forecasts must make assumptions about both technologies and social systems. At their most successful, they influence how people act by showing the consequences of not acting. They are useful when they provide insights to energy planners, influence the perceptions of the public and the energy policy community, capture current understanding of underlying physical and economic principles, or highlight key emerging social or economic trends. It is true that at best we see dimly into the future, but those who acknowledge their duty to posterity will feel impelled to use their foresight upon what facts and guiding principles we do possess. Though many data are at present wanting or doubtful, our conclusions may be rendered so far probable as to lead to further inquiries… ( 1 ), p. 4.