Decision support system for power systems applying load forecasting and the learning of causal relationships

  • Authors:

  • Cláudio A. Rocha;Ádamo L. De Santana;Carlos Renato L. Francês;Eloi Favero;Valquiria Macedo;Ubiratan H. Bezerra;Armando Tupiassú;Vanja Gato;Liviane Rego;Rafael Costa;Cibelle Nascimento

  • Affiliations:

  • Center of Natural and Technological Sciences, University of the Amazon, Belém, Pará, Brazil;Department of Electric Engineering and Computing, Federal University of Pará, Belém, Pará, Brazil;Department of Electric Engineering and Computing, Federal University of Pará, Belém, Pará, Brazil;Department of Electric Engineering and Computing, Federal University of Pará, Belém, Pará, Brazil;Department of Electric Engineering and Computing, Federal University of Pará, Belém, Pará, Brazil;Department of Electric Engineering and Computing, Federal University of Pará, Belém, Pará, Brazil;Rede Celpa, Belé, Pará, Brazil;Rede Celpa, Belé, Pará, Brazil;Department of Electric Engineering and Computing, Federal University of Pará, Belém, Pará, Brazil;Center of Natural and Technological Sciences, University of the Amazon, Belém, Pará, Brazil;Department of Electric Engineering and Computing, Federal University of Pará, Belém, Pará, Brazil

  • Venue:
  • MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents a decision support system for power load forecast and learning of causal relationships 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 the socio-economic and climatic data of the region.