The Home Energy Saver (HES) suite offers popular online simulation tools that enable U.S. homeowners and energy professionals to rigorously evaluate home energy use and develop recommendations on how energy can be saved across all end uses. The underlying analytical system is also available as a web service to power third-party energy analysis tools. Given the system's diverse uses, it is important that the simulation is robust and intrinsically accurate. While the engineering methods and assumptions are extensively documented and subjected to peer review, it is useful to evaluate how well HES predicts energy use in occupied homes. In this paper we compare measured to predicted energy use for 428 occupied homes in Oregon, Florida, and Wisconsin, representing a diversity building types, energy intensities, and occupant behaviors. We show how audit depth, knowledge of operational details, and submetered energy data can be valuable to the process of improving model accuracy—particularly for individual households, where energy use can vary three-fold for homes with virtually identical physical characteristics. Accuracy is strongly proportional to the quality and completeness of inputs, yet audit data are often deficient. Predictions are best—and the tendency of models to over-predict actual consumption is mitigated—when behavioral inputs match actual conditions. We find that Averaged across groups of homes, HES predicts energy use within 1% of actual consumption when physical characteristics and occupant behavior are well accounted for. New research findings are conferring even greater accuracy as they are incorporated into simulation tools.