论文信息 - Short Term Load Forecasting via a Hierarchical Neural Model

Short Term Load Forecasting via a Hierarchical Neural Model

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 which the context information given by former events plays a primary role. The model is trained and compared to a Multi-Layer Perceptron load forecaster. It is required to compute the one to twenty four steps ahead recursive load forecasts. The paper presents the results, and evaluates them.

A. P. Alves da Silva | Agnaldo José da Rocha Reis | Otvio A. S. Carpinteiro

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