Utilities have deployed tens of millions of smart meters, which record and transmit home energy usage at fine-grained intervals. These deployments are motivating researchers to develop new energy analytics that mine smart meter data to learn insights into home energy usage and behavior. Unfortunately, a significant barrier to evaluating energy analytics is the overhead of instrumenting homes to collect aggregate energy usage data and data from each device. As a result, researchers typically evaluate their analytics on only a small number of homes, and cannot rigorously vary a home's characteristics to determine what attributes of its energy usage affect accuracy. To address the problem, we develop SmartSim, a publicly-available device-accurate smart home energy trace generator. SmartSim generates energy usage traces for devices by combining a device energy model, which captures its pattern of energy usage when active, with a device usage model, which specifies its frequency, duration, and time of activity. SmartSim then generates aggregate energy data for a simulated home by combining the data from each device. We integrate SmartSim with NILM-TK, a publicly-available toolkit for Non-Intrusive Load Monitoring (NILM), and compare its synthetically generated traces with traces from a real home to show they yield similar quantitative and qualitative results for representative energy analytics.