Load models for short-term load forecasting in multinode power systems

The problem of on-line short-term load demand forecasting at all the major loading nodes of an interconnected power system is studied. A multivariable state space model is proposed. The innovations representation is utilized, so that an ordinary recursive least-squares algorithm can be used to give unbiased estimates of the model parameters. State estimation is carried out by using adaptive estimation techniques. The parameter identification and state estimation algorithms are combined in a bootstrap mannar. A scheme for detecting abnormal load behaviour is proposed. A weather sensitive load model is also presented for longer lead time forecasts. Actual data provided by the Ontario Hydro for four loading buses is used for five-minute, ten-minute and hourly forecasts. The results show that the proposed approach is very attractive.