1. A method for identifying a model of a dynamic system, comprising: identifying a number of models of the dynamic system, wherein each respective one of the number of models corresponds to a different zone of the dynamic system and includes a sub-model that is a sub-zone of the dynamic system, and wherein each respective zone includes a thermostat; estimating a number of parameters for each of the number of models of the dynamic system, wherein the number of parameters include co-related parameters between the number of models, parameters of the model to which each respective zone corresponds, and interaction parameters; predicting an output variable using the estimated number of parameters for each of the number of models; calculating a rate of error of the predicted output variable for each of the number of models compared to an observed output variable, wherein the calculated rate of error includes an aggregate total of a number of error residuals for the output variable minimized for a desired prediction horizon and a number of one-step correction predictions; identifying a best model among a number of well performing models of the dynamic system based on the calculated rate of error for each of the number of models, wherein the number of well performing models includes a threshold number of models; combining the best model among the number of well performing models with another model among the number of well performing models to identify a revised best model, wherein the revised best model has a lower aggregate total of a number of error residuals for the output variable minimized for a desired prediction horizon and a number of one-step correction predictions than the best model; using the revised best model to control a controller of the dynamic system; and exploring new parameters using a surrogate model based on the best model, wherein the surrogate model: graphs the number of parameters on an x-axis of a graph and a number of rates of errors of the number of models on a y-axis of the graph; fits the graphed number of parameters and error rates to a regression model; and explores the new parameters based on a result of the fitting to the regression model.