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Novel autoregressive basis structure model for short-term forecasting of customer electricity demand

This paper describes the method of a prototype forecast component of the energy resource management control algorithm for STATCOMs with battery energy storage. It is desired to be computationally efficient and of minimal complexity due to the desired purposes of forecasting each load in a LV network. The forecast model is comprised of a basis structure selected from observed electricity demand data and an electricity demand difference forecasting component estimated by the autoregressive method. The produced forecasting model had a R2 of 0.65 and a standard error of 368.55 W. During validation of the model, discrepancies between the forecasted and observed electricity demand profiles were observed. To overcome forecast model limitations, future work will involve more precise clustering of demand profiles according to additional temporal and environmental variables. This is to enable forecasts under a more diverse range of electricity demand profiles. The final developed forecasting model will be a core component of the firmware controlling STATCOMS with energy storage systems.