Models can help architects design effective and secure advanced metering infrastructure (AMI) deployments. Because of the complex interactions among the numerous smart meters, smart home devices, customers, the utility, and potential adversaries, the models are often complex and have long execution times. In addition, the models often contain a large number of uncertain input variables.Modelers seek to understand the impact of uncertain input variables on the model through the use of sensitivity analysis (SA) and uncertainty quantification (UQ). However, long-running models are not amenable to such techniques, since they require that the model be run many times. One approach to help overcome this challenge is to build a metamodel (a model of the model) that accurately emulates the original model but is much faster.In this paper, we explain an approach we developed to do fast and thorough SA and UQ using a specially designed metamodel of stacked regressors that can be used to analyze AMI models. We demonstrated the approach by applying it to a complex AMI security model. We show that our metamodel is substantially faster than the base AMI model, more accurate than other existing metamodel approaches, and amenable to SA and UQ.