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Proceedings Paper

Decision support in power systems based on load forecasting models and influence analysis of climatic and socio-economic factors
Author(s): Cláudio A. Rocha; Ádamo L. de Santana; Carlos R. Francês; Ubiratan Bezerra; Armando Tupiassú; Vanja Gato; Liviane Rego; João Costa
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Paper Abstract

This paper presents a decision support system for power load forecast and the learning of influence patterns of the socio-economic and climatic factors on the power consumption based on mathematical and computational intelligenge methods, with the purpose of defining the future power consumption of a given region, as well as to provide a mean for the analysis of correlations between the power consumption and these factors. Here we use a linear modelo of regression for the forecasting, also presenting a comparative analysis with neural networks, to prove its efectiveness; and also Bayesian networks for the learning of causal relationships from the data.

Paper Details

Date Published: 12 October 2006
PDF: 10 pages
Proc. SPIE 6383, Wavelet Applications in Industrial Processing IV, 63830I (12 October 2006); doi: 10.1117/12.686433
Show Author Affiliations
Cláudio A. Rocha, Univ.of the Amazon (Brazil)
Ádamo L. de Santana, Federal Univ. of Para (Brazil)
Carlos R. Francês, Federal Univ. of Para (Brazil)
Ubiratan Bezerra, Federal Univ. of Para (Brazil)
Armando Tupiassú, Power Supplier of the State of Para (Brazil)
Vanja Gato, Power Supplier of the State of Para (Brazil)
Liviane Rego, Federal Univ. of Para (Brazil)
João Costa, Federal Univ. of Para (Brazil)


Published in SPIE Proceedings Vol. 6383:
Wavelet Applications in Industrial Processing IV
Frédéric Truchetet; Olivier Laligant, Editor(s)

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