An Intelligent Approach for Medium Term Hydropower Scheduling Using Ensemble Model

  • Thais Gama de Siqueira
  • Ricardo Menezes Salgado
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 99)


The medium term hydropower scheduling (MTHS) problem involves an attempt to determine, for each time stage of the planning period, the amount of generation at each hydro plant which will maximize the expected future benefits throughout the planning period, while respecting plant operational constraints. Besides, it is important to emphasize that this decision-making has been done based mainly on inflow earliness knowledge. To perform the forecast of a determinate basin, it is possible to use some intelligent computational approaches. In this paper one considers the Dynamic Programming (DP) with the inflows given by their average values, thus turning the problem into a deterministic one which the solution can be obtained by deterministic DP (DDP). The performance of the DDP technique in the MTHS problem was assessed by simulation using the ensemble prediction models. Features and sensitivities of these models are discussed.


Medium Term Hydropower Scheduling Dynamic Programming Inflow Forecast Artificial Intelligence Predictive Models Ensembles 


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  1. 1.
    Pereira, M.V.F.: Optimal Scheduling of Hydrothermal System – An Overview. In: IFAC Symposium on Planning and Operation of Electric Energy Systems, Rio de Janeiro, pp. 1–9 (1985)Google Scholar
  2. 2.
    Araripe Neto, T.A., Cotia, C.B., Pereira, M.V.F., Kelman, J.: Comparison of Stochastic and Deterministic Approches in Hydrothermal Generation Scheduling. In: IFAC Symposium on Planning and Operaion of Electric Energy Systems, Rio de Janeiro, Brazil (1985)Google Scholar
  3. 3.
    Arvanitidis, N.V., Rosing, J.: Composite representation of a multireservoir hydroelectric power system. IEEE Transactions on Power Apparatus and Systems PAS-89, 319–326 (1970)Google Scholar
  4. 4.
    Dagli, C.H., Miles, J.F.: Determining Operating Policies for a Water Resources System. Journal of Hydrology 47, 297–306 (1980)CrossRefGoogle Scholar
  5. 5.
    Bellman, R.E.: Dynamic Programming. Princeton University Press, Princeton (1957)zbMATHGoogle Scholar
  6. 6.
    Bertsekas, D.P.: Dynamic Programming: Deterministic and Stochastic Models. Academic Press, London (1987)zbMATHGoogle Scholar
  7. 7.
    Stedinger, J.R., Sule, B.F., Loucks, D.P.: Stochastic Dynamic Programming Models for Reservoir Operation Optimization. Water Resources Research 20(11), 1499–1505 (1984)CrossRefGoogle Scholar
  8. 8.
    Martinez, L., Soares, S.: Comparison between Closed-Loop and Partial Open-Loop Feedback Control Policies in Long Term Hydrothermal Scheduling. IEEE Transactions on Power Systems 17(2) (2002)Google Scholar
  9. 9.
    Perrone, M.P.: Improving regression estimates: Averaging methods for variance reduction with extensions to general convex measure optimization. PhD Thesis, Brown University (1993)Google Scholar
  10. 10.
    Reid, D.J.: Combining three estimates of gross domestic product. Economics 35, 431–444 (1968)Google Scholar
  11. 11.
    Bates, J.M., Granger, G.W.J.: The combination of forecasts. Operations Research Quaterly 20, 451–468 (1969)CrossRefGoogle Scholar
  12. 12.
    Kang, B.H.: Unstable weights in the combination of forecasts. Management Science 32, 683–695 (1986)CrossRefGoogle Scholar
  13. 13.
    Baxt, W.G.: Improving the accuracy of an artificial neural network using multiple differently trained networks. Neural Computation 4(5), 135–144 (1992)CrossRefGoogle Scholar
  14. 14.
    Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  15. 15.
    Hansen, L., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)CrossRefGoogle Scholar
  16. 16.
    Sharkey, A. (ed.): Combining artificial neural nets: Ensemble and modular multi-net systems. Springer, London (1999)zbMATHGoogle Scholar
  17. 17.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  18. 18.
    Koza, J.R.: Survey of genetic algorithms and genetic programming. In: Proceedings of the Wescon 95 - Conference Record: Microelectronics, Communications Technology, Producing Quality Products, Mobile and Portable Power, Emerging Technologies, San Francisco, CA, November 7–9. IEEE, New York (1995)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thais Gama de Siqueira
    • 1
  • Ricardo Menezes Salgado
    • 2
  1. 1.Institute of Science and TechnologyUniversity of AlfenasPoços de CaldasBrazil
  2. 2.Institute of Exact SciencesFederal University of AlfenasAlfenasBrazil

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