Decision support in power systems based on load forecasting models and influence analysis of climatic and socio-economic factors
Cláudio Alex Jorge da Rocha, Ádamo Lima de Santana, Carlos Renato Lisboa Francês, Ubiratan Holanda Bezerra, Armando Tupiassú, Vanja Gato, Liviane Rego, João C. W. A. Costa · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
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.