Two New Algorithms for On-Line Modelling and Forecasting of the Load Demand of a Multinode Power System

Two on-line algorithms are proposed for modelling and forecasting short-term multiple load demand. First a multivariable time series model is presented with a systematic method for determining its order and estimating its parameters. Another model based on the state variable form is then considered. Two decoupled algorithms, recursive least-squares and adaptive Kalman filtering, are combined in a bootstrap manner to estimate the model parameters and states. The performance of the two methods is compared using data provided by the Ontario Hydro for four loading nodes.