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Erschienen in:
2006 | OriginalPaper | Buchkapitel
A Hybrid Neural Model in Long-Term Electrical Load Forecasting
verfasst von : Otávio A. S. Carpinteiro, Isaías Lima, Rafael C. Leme, Antonio C. Zambroni de Souza, Edmilson M. Moreira, Carlos A. M. Pinheiro
Erschienen in: Artificial Neural Networks – ICANN 2006
Verlag: Springer Berlin Heidelberg
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A novel hierarchical hybrid neural model to the problem of long-term electrical 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 which require time series analysis. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are endowed with time windows in their input layers. They are trained and assessed on load data extracted from a North-American electric utility. The models are required to predict once every week the electric peak-load and mean-load during the next two years. The results are presented and evaluated in the paper.