A hierarchical neural model in short-term load forecasting

This paper proposes a novel neural model for the short-term load forecasting problem. The neural model is made up of two self-organizing map nets-one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on the load data extracted from a Brazilian electric utility. It was required to predict once every hour the electric load during the next 24 hours. The paper presents the results and evaluates them.