A hierarchical neural model in short-term load forecasting
This paper proposes a novel neural model to the problem of short-term load forecasting (STLF). The neural model is made up of two self-organizing map (SOM) netsone 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 on load data extracted from a Brazilian electric utility, and compared to a multilayer perceptron (MLP) load forecaster. It was required to predict once every hour the electric load during the next 24h. The paper presents the results, the conclusions, and points out some directions for future work.
short-term load forecasting self-organizing map neural network