Data analysis by means of Kohonen feature maps for load forecast in power systems

Because of the clustering and dimensionality reduction abilities the Kohonen feature map (KFM) can be the preferable tool for deriving knowledge about dependencies of the load consumption in electrical energy systems (EES). This paper describes the application of the KFM for analysing and splitting extensive load databases. The objective is to get separate clusters of load shapes for making short term load forecast (STLF) models with a high accuracy. The building of the forecast models is based on feedforward neural networks (NN).<>