A hierarchical self-organizing map model in short-term load forecasting

This paper proposes a novel neural model to the problem of short-term load forecasting. 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 whi ch the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian ele ctric utility. It was required to predict once every hour the electric load during the next 24 hours. The paper presents the results, and evaluates th em.