Gauss-Markov models for forecasting and risk evaluation

Abstract

In power systems, expansion of generating and transmission facilities, day-to-day operation are dependent on the future loading demands. Load uncertainty can be modeled using the Gauss-Markov properties for a random process. Sharing the Gauss-Markov characteristics, Box-Jenkins (ARIMA) forecast procedure is described. Then, electricity consumption data is simulated as an application of ARIMA models. Finally, a risk evaluation study using a Gauss-Markov load model is also demonstrated.