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Static state estimation in electric power systems is normally accomplished without the use of time-history data or prediction. This paper presents preliminary work on the use of the discrete-time Kalman filter to incorporate time history and power demand prediction into state estimators. The problem of state estimation combined with the knowledge of the forecasted load is posed as a Kalman filtering problem using a novel discrete-time model. The model relates current and previous states using the electric power flow equations. An IEEE 14-bus test system example is used to illustrate the potential for enhanced performance of such Kalman filter-based state estimation.