Identification of stochastic electric load models from physical data

Abstract

The three step identification process of model development, parameter estimation, and performance analysis is illustrated through the identification of models for the prediction of electric power demand. Each step is carefully supported by numerical results based on physical data. Three types of progressively more complex but more accurate load models are identified which describe 1) time periodicity, 2) time periodicity plus load autocorrelation, and 3) time periodicity plus load autocorrelation plus dynamic temperature effects. Accurate predictions up to one week are demonstrated. General guidelines are extrapolated from this identification example when possible.