Building energy simulation tools are now being used in a number of new roles such as building operation optimization, performance verification for efficiency programs, and - recently- building energy code analysis, design, and compliance verification in the residential sector. But increasing numbers of studies show major differences between the results of these simulations and the actual measured performance of the buildings they are intended to model. The accuracy and calibration of building simulations have been studied extensively in the commercial sector, but these new applications have created a need to better understand the performance of home energy simulations. In this paper, we assess the ability of the DOE's EnergyPlus software to simulate the energy consumption of 106 homes using audit records, homeowner survey records, and occupancy estimates taken from monitored data. We compare the results of these simulations to device-level monitored data from the actual homes to provide a first measure of the accuracy of the EnergyPlus condensing unit, central air supply fan, and other energy consumption model estimates in a large number of homes. We then conduct sensitivity analysis to observe which physical and behavioral characteristics of the homes and homeowners most influence the accuracy of the modeling. Results show that EnergyPlus models do not accurately or consistently estimate occupied whole-home energy consumption. While some models accurately predict annual energy consumption to within 1% of measured data, none of the modeled homes meet ASHRAE criteria for a calibrated model when looking at hourly interval data. The majority of this error is due to appliance and lighting energy overestimates, followed by AC condensing unit use. These inaccuracies are due to factors such as occupant behaviors and differences in appliance and lighting stocks which are not well-captured in traditional energy audit reports. We identify a number of factors which must be specified for an accurate model, and others where using a default value will produce a similar result. The use of building simulation tools reflects a shift from a component-focused approach to a systems approach to residential code analysis and compliance verification that will serve to better identify and deploy efficiency measures in homes. By better understanding the limitations of home energy simulations and adopting strategies to mitigate the effects of model errors, simulation models can serve as valuable decision making tools in the residential sector. (C) 2017 The Authors. Published by Elsevier