A hierarchical hybrid neural model in short-termload 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 —, 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 was trained and assessed on load data extracted from a Brazilian electric utility. It was required to predict once every hour the electric load during the next six hours. The paper presents the results,

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