A hierarchical hybrid neural model with time integrators in long-term peak-load forecasting

A novel hierarchical hybrid neural model to the problem of long-term peak-load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets - one on top of the other -, and a single-layer perceptron. It has application into domains in which the context information given by former events plays a primary role. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are trained and assessed on load data extracted from a North-American electric utility. They are required to predict either once every week or once every month the electric peak-load during the next two years. The results are presented and evaluated in the paper.