An Application of State Estimation to Short-Term Load Forecasting, Part I: Forecasting Modeling

Abstract

A precise short-term forecasting method for estimating the status of systems is required for on-line real-time control of complex power systems. In this paper, some state estimation type modelings of load forecasting are introduced, and a few practical problems for applying state estimation are discussed. In [11] the identification algorithms of the covariance matrices of system and observation noise are developed using observed data series, and their experimental results by simulation model are discussed. Results show that forecasting error by the developed method is quickly converged to minimum error of the ideal state estimation with previously known noise properties.