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A hierarchical neural model with time windows in long-term electrical load forecasting

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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 for the next 2 years. The results are presented and evaluated in the paper.

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Acknowledgment

This research is supported by CNPq, Brazil.

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Authors and Affiliations

  1. Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, 37500-903, Itajuba, MG, Brazil

    Otávio A. S. Carpinteiro, Isaías Lima, Rafael C. Leme, Antonio C. Zambroni de Souza, Edmilson M. Moreira & Carlos A. M. Pinheiro

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  1. Otávio A. S. Carpinteiro
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  2. Isaías Lima
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  3. Rafael C. Leme
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  4. Antonio C. Zambroni de Souza
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  5. Edmilson M. Moreira
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  6. Carlos A. M. Pinheiro
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Correspondence to Otávio A. S. Carpinteiro.

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Carpinteiro, O.A.S., Lima, I., Leme, R.C. et al. A hierarchical neural model with time windows in long-term electrical load forecasting. Neural Comput & Applic 16, 465–470 (2007). https://doi.org/10.1007/s00521-006-0072-8

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  • DOI: https://doi.org/10.1007/s00521-006-0072-8

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