Abstract
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.
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Carpinteiro, O.A.S., Lima, I., Leme, R.C., de Souza, A.C.Z., Moreira, E.M., Pinheiro, C.A.M. (2006). A Hybrid Neural Model in Long-Term Electrical Load Forecasting. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_75
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DOI: https://doi.org/10.1007/11840930_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
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