Dynamic one step ahead prediction of electricity loads at suburban level

One step ahead prediction based on short time series is presented. It will be shown here first that for the subject of short term prediction of electricity load, even though a large a-mount of data may be available, only the most recent of it may be of importance. That gives rise to prediction based on limited amount of data. We here propose implementation of some instances of architectures of artificial neural networks as potential systematic solution of that problem as opposed to heuristics that are in use. To further rise the dependability of the predicted data averaging of two independent predictions is proposed. Examples will be given related to short-term (hourly) forecasting of the electricity load at suburban level. Prediction is carried out on real data taken for one suburban transformer station. Implementation of an on-line real time prediction system is presented.