SciELO - Scientific Electronic Library Online

 
vol.27 número2Detecção de fraudes na distribuição de energia elétrica utilizando support vector machineComparação da Análise de Componentes Principais e da CATPCA na Avaliação da Satisfação do Passageiro de uma Transportadora Aérea índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Artigo

Indicadores

Links relacionados

  • Em processo de indexaçãoCitado por Google
  • Não possue artigos similaresSimilares em SciELO
  • Em processo de indexaçãoSimilares em Google

Compartilhar


Investigação Operacional

versão impressa ISSN 0874-5161

Inv. Op. v.27 n.2 Lisboa dez. 2007

 

Previsão dos Preços da Energia Eléctrica através de Redes Neuronais Artificiais

 

João Catalão †

Sílvio Mariano †

Victor Mendes ‡

Luís Ferreira §

 

† Departamento de Engenharia Electromecânica

UBI– Universidade da Beira Interior

Catalao@ubi.pt

sm@ubi.pt

 

‡ Departamento de Engenharia Electrotécnica e Automação

ISEL – Instituto Superior de Engenharia de Lisboa

vfmendes@isel.pt

 

§ Departamento de Engenharia Electrotécnica e de Computadores

IST – Instituto Superior Técnico

lmf@ist.utl.pt

 

Resumo

Neste artigo é apresentada uma ferramenta computacional, baseada em redes neuronais artificiais, para a previsão dos preços da energia eléctrica no apoio à decisão em ambiente competitivo. Apresentam-se os resultados numéricos obtidos para um caso de estudo, e conclui-se sobre o desempenho da ferramenta computacional proposta comparativamente a uma abordagem baseada em séries temporais.

 

Title: Electricity prices forecasting through artificial neural networks

 

Abstract

In this paper, a computational tool based on artificial neural networks is presented for electricity prices forecasting to support decision making in a competitive environment. The numerical results obtained for a case study illustrate the behaviour of the computational tool proposed comparatively to a time-series approach.

Keywords: Price forecasting, Neural network, Levenberg-Marquardt algorithm

 

Texto completo disponível apenas em PDF.

Full text only available in PDF format.

 

Referências

Almeida, L. B. (1997) Multilayer Perceptrons, Handbook of Neural Computation, Oxford University Press, UK.        [ Links ]

Angelus, A. (2001) Electricity Price Forecasting in Deregulated Markets, Electricity Journal, Vol. 14, No. 3, pp. 32-41.

Bastian, J., Zhu, J., Banunarayanan, V., and Mukerji, R. (1999) Forecasting Energy Prices in a Competitive Market, IEEE Computer Applications Power, Vol. 12, No. 3, pp. 40-45.

Bunn, D. W. (2000) Forecasting Loads and Prices in Competitive Power Markets, Proceedings of the IEEE, Vol. 88, No. 2, pp. 163-169.

Catalão, J. P. S. (2006) Novas Metodologias de Optimização em Sistemas de Energia Hidrotérmicos, Dissertação de Doutoramento, Universidade da Beira Interior, Covilhã.

Catalão, J. P. S., Mariano, S. J. P. S., Mendes, V. M. F., and Ferreira, L. A. F. M. (2006) Application of Neural Networks on Next-Day Electricity Prices Forecasting, Proceedings of the 41st International Universities Power Engineering Conference, Newcastle upon Tyne, UK.

Conejo, A. J., Plazas, M. A., Espínola, R., and Molina, A. B. (2005) Day-Ahead Electricity Price Forecasting using the Wavelet Transform and ARIMA Models, IEEE Transactions on Power Systems, Vol. 20, No. 2, pp. 1035-1042.

Contreras, J., Espínola, R., Nogales, F. J., and Conejo, A. J. (2003) ARIMA Models to Predict Next-Day Electricity Prices, IEEE Transactions on Power Systems, Vol. 18, No. 3, pp. 1014-1020.

Fosso, O. B., Gjelsvik, A., Haugstad, A., Mo, B., and Wangensteen, I. (1999) Generation Scheduling in a Deregulated System. The Norwegian Case, IEEE Transactions on Power Systems, Vol. 14, No. 1, pp. 75-80.

Garcia, R. C., Contreras, J., van Akkeren, M., and Garcia, J. B. C. (2005) A GARCH Forecasting Model to Predict Day-Ahead Electricity Prices, IEEE Transactions on Power Systems, Vol. 20, No. 2, pp. 867-874.

Hagan, M. T. and Menhaj, M. B. (1994) Training Feedforward Networks with the Marquardt Algorithm, IEEE Transactions on Neural Networks, Vol. 5, No. 6, pp. 989-993.

Haykin, S. (1999) Neural Networks: A Comprehensive Foundation, Prentice-Hall, New Jersey, USA.

Hippert, H. S., Pedreira, C. E., and Souza, R. C. (2001) Neural Networks for Short-Term Load Forecasting: A Review and Evaluation, IEEE Transactions on Power Systems, Vol. 16, No. 1, pp. 44-55.

Nogales, F. J., Contreras, J., Conejo, A. J., and Espínola, R. (2002) Forecasting Next-Day Electricity Prices by Time Series Models, IEEE Transactions on Power Systems, Vol. 17, No. 2, pp. 342-348.

Principe, J. C., Euliano, N. R., and Lefebvre, W. C. (2000) Neural and Adaptive Systems: Fundamentals Through Simulations, Wiley, New York, USA.

Rodriguez, C. P. and Anders, G. J. (2004) Energy Price Forecasting in the Ontario Competitive Power System Market, IEEE Transactions on Power Systems, Vol. 19, No. 1, pp. 366-374.

Saini, L. M. and Soni, M. K. (2002) Artificial Neural Network Based Peak Load Forecasting using Levenberg-Marquardt and Quasi-Newton Methods, IEE Proceedings-Generation Transmission and Distribution, Vol. 149, No. 5, pp. 578-584.

Shahidehpour, M., Yamin, H., and Li, Z. (2002) Market Operations in Electric Power Systems: Forecasting, Scheduling and Risk Management, Wiley, New York, USA.

Szkuta, B. R., Sanabria, L. A., and Dillon, T. S. (1999) Electricity Price Short-Term Forecasting using Artificial Neural Networks, IEEE Transactions on Power Systems, Vol. 14, No. 3, pp. 851-857.

Wang, A. J. and Ramsay, B. (1998) A Neural Network Based Estimator for Electricity Spot-Pricing with Particular Reference to Weekend and Public Holidays, Neurocomputing, Vol. 23, No. 1, pp. 47-57.

Yamin, H. Y., Shahidehpour, S. M., and Li, Z. (2004) Adaptative Short-Term Electricity Price Forecasting using Artificial Neural Networks in the Restructured Power Markets, International Journal of Electrical Power & Energy Systems, Vol. 26, No. 8, pp. 571-581.

Zhou, M., Yan, Z., Ni, Y. X., Li, G., and Nie, Y. (2006) Electricity Price Forecasting with Confidence-Interval Estimation through an Extended ARIMA Approach, IEE Proceedings-Generation Transmission and Distribution, Vol. 153, No. 2, pp. 187-195.