Modelo não-linear de longo prazo para a potência requerida do sistema brasileiro de eletricidade

The formerly closed Brazilian electrical energy market has been disclosed for investors and domestic consumers, which now have choices for theirs decisions concerning energy sales and purchasing, in long-term contracts. To establish minimal planning condition, suppliers need to forecast the required load in order to project and operate power plants. With the aid of a method that integrates non-linear regression and soft-system methodology, we depict a non-linear model for the gross total electric power requirement, counting on domestic activity, population and human developing index. A goodness-of-fit better then 96% was achieved. The final analysis focused on the strong contingency period occurred in Brazil in 2001.

[1]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[2]  Luis C. Dias,et al.  Using SSM to rethink the analysis of energy efficiency initiatives , 2004, J. Oper. Res. Soc..

[3]  D.W. Bunn,et al.  Forecasting loads and prices in competitive power markets , 2000, Proceedings of the IEEE.

[4]  George G. Karady,et al.  Advancement in the application of neural networks for short-term load forecasting , 1992 .

[5]  C. Montserrat,et al.  Imagens da organização , 2007 .

[6]  A. D. Patton,et al.  Development of an intelligent long-term electric load forecasting system , 1996, Proceedings of International Conference on Intelligent System Application to Power Systems.

[7]  Valerie Belton,et al.  Integrated Support from Problem Structuring through to Alternative Evaluation Using COPE and V·I·S·A , 1997 .

[8]  Fernando Pereira Tostes PROGRAMAÇÃO NÃO LINEAR - P.N.L. , 1997 .

[9]  S. M. El-Debeiky,et al.  Long-Term Load Forecasting for Fast-Developing Utility Using a Knowledge-Based Expert System , 2002, IEEE Power Engineering Review.

[10]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[11]  J. Hair Multivariate data analysis , 1972 .

[12]  S. A Generalized Knowledge-Based Short-Term Load-Forecasting Technique , .

[13]  Miguel Afonso Sellitto Processos de pensamento da TOC como alternativa sistêmica de análise organizacional: uma aplicação em saúde pública , 2005 .

[14]  Peter Checkland,et al.  Systems Thinking, Systems Practice , 1981 .

[15]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[16]  Leonardo Santos Caio Análise das Metodologias de Previsão de Mercado de Energia Elétrica: Relações Macroeconômicas e o Novo Perfil de Planejamento no Ambiente Pós-Privatização , 1998 .

[17]  Leonardo Ensslin,et al.  Decision Support Systems in action: Integrated application in a multicriteria decision aid process , 1999, Eur. J. Oper. Res..

[18]  Gerald I. Kendall Securing the Future: Strategies for Exponential Growth Using the Theory of Constraints , 1997 .

[19]  Xie Da,et al.  The physical series algorithm of mid-long term load forecasting of power systems , 2000 .

[20]  Eliyahu M. Goldratt,et al.  Theory of Constraints , 1990 .

[21]  Ibrahim El-Amin,et al.  Artificial neural networks as applied to long-term demand forecasting , 1999, Artif. Intell. Eng..