Kenneth H. Tiedemann
statistically adjusted engineering estimates, identification, modelling
Utilities and their regulators have become increasingly interested in the accurate estimation of the impacts of energy efficiency programs. Simple engineering models have significant limitations which have been addressed by combing engineering and energy use data in statistically adjusted engineering (SAE) models used to identify realization rates for energy savings technologies. This paper extends the SAE methodology and contributes to this literature in two ways. First, the initial engineering estimates are recalibrated by using detailed end-use metering data, sorted by space type within building type, where this data includes 3,290 data points. This provides a high degree of resolution for hours of use by technology. Second, the analysis matches each participant site with a comparable non-participant site. This matching helps to control for variables omitted from the model, since the matched pairs have similar characteristics for the omitted variables.
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