A computer-implemented method of modeling and monitoring an energy load of a physical system, the method characterized by: defining a dependent variable with a load monitoring server, the dependent variable representing an operation of the energy load; defining at least one independent variable (222, 224, 226, 244, 246, 262) with the load monitoring server, the at least one independent variable representing at least one influencing driver of the operation of the energy load; defining at least one partition variable with the load monitoring server, the at least one partition variable representing an operating condition of the energy load as a set of two or more discrete values; receiving a reference dataset (290a-g) at the load monitoring server, the reference dataset including coincident values of the dependent variable, at least one independent variable, and at least one partition variable; analyzing the reference dataset with the load monitoring server to arrange the reference dataset into interdependent data based upon the discrete values of the at least one partition variable; creating a model for each discrete value of the at least one partition variable in the analyzed reference dataset with the load monitoring server in which the dependent variable is modeled as a function of the at least one independent variable, the model representing operation of the energy load; receiving an input dataset (390a-390g) at the load monitoring server, the input dataset including additional coincident values of the at least one independent variable and the at least one partition variable; processing the additional coincident values of the at least one independent variable and the at least one partition variable with the created models; and generating an output dataset with the load monitoring server from the created models, the output dataset including predicted dependent variable values from the at least one independent variable and the at least one partition variable from the input dataset. defining a dependent variable with a load monitoring server, the dependent variable representing an operation of the energy load; defining at least one independent variable (222, 224, 226, 244, 246, 262) with the load monitoring server, the at least one independent variable representing at least one influencing driver of the operation of the energy load; defining at least one partition variable with the load monitoring server, the at least one partition variable representing an operating condition of the energy load as a set of two or more discrete values; receiving a reference dataset (290a-g) at the load monitoring server, the reference dataset including coincident values of the dependent variable, at least one independent variable, and at least one partition variable; analyzing the reference dataset with the load monitoring server to arrange the reference dataset into interdependent data based upon the discrete values of the at least one partition variable; creating a model for each discrete value of the at least one partition variable in the analyzed reference dataset with the load monitoring server in which the dependent variable is modeled as a function of the at least one independent variable, the model representing operation of the energy load; receiving an input dataset (390a-390g) at the load monitoring server, the input dataset including additional coincident values of the at least one independent variable and the at least one partition variable; processing the additional coincident values of the at least one independent variable and the at least one partition variable with the created models; and generating an output dataset with the load monitoring server from the created models, the output dataset including predicted dependent variable values from the at least one independent variable and the at least one partition variable from the input dataset.