Abstract
The paper presents the neural network approach to the precise 24-hour load pattern prediction for the next day in the power system. In this approach we use the ensemble of few neural network predictors working in parallel. The predicted series containing 24 values of the load pattern generated by the neural predictors are combined together using principal component analysis. Few principal components form the input vector for the final stage predictor composed of another neural network. The developed system of prediction was tested on the real data of the Polish Power System. The results have been compared to the appropriate values generated by other methods.
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Cottrell, M., Girard, B., Girard, Y., Muller, C., Rousset, P.: Daily electrical power curve: classification and forecasting using a Kohonen map. In: Sandoval, F., Mira, J. (eds.) IWANN 1995. LNCS, vol. 930, pp. 1107–1113. Springer, Heidelberg (1995)
Diamantras, K., Kung, S.Y.: Principal component neural networks. Wiley, N.Y (1996)
Fidalgo, J.N., Pecas Lopez, J.: Load forecasting performance enhancement when facing anomalous events. IEEE Trans. Power Systems 20, 408–415 (2005)
Gonzalez-Romera, E., Jaramillo-Moran, M.A., Carmona-Fernandez, D.: Monthly electric energy demand forecasting based on trend extraction. IEEE Trans. Power Systems 21, 1946–1953 (2006)
Haykin, S.: Neural networks, a comprehensive foundation. Macmillan, N.Y (2002)
Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. on Power Systems 16, 44–55 (2001)
Kandil, N., Wamkeue, R., Saad, M., Georges, S.: An efficient approach for short term load forecasting using artificial neural networks. Electrical Power and Energy Systems 28, 525–530 (2006)
Kuntcheva, L.: Combining pattern classifiers - methods and algorithms. Wiley, New Jersey (2004)
Mandal, P., Senjyu, T., Urasaki, N., Funabashi, T.: A neural network based several hours ahead electric load forecasting using similar days approach. Electrical Power and Energy Systems 28, 367–373 (2006)
Osowski, S., Siwek, K.: The self-organizing neural network approach to load forecasting in power system. In: Int. Joint Conf. on Neural Networks, Washington, pp. 1345–1348 (1999)
Osowski, S., Siwek, K.: Regularization of neural networks for load forecasting in power system. In: IEE Proc. GTD, vol. 149, pp. 340–345 (2002)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Sorjamaa, A., Hao, J., Reyhani, N., Li, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocomputing 70, 2861–2869 (2007)
Yalcinoz, T., Eminoglu, U.: Short term and medium term power distribution load forecasting by neural networks. Energy Conversion and Management 46, 1393–1405 (2005)
Matlab manual, user’s guide, MathWorks, Natick (2002)
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Siwek, K., Osowski, S. (2009). Two-Stage Neural Network Approach to Precise 24-Hour Load Pattern Prediction. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_39
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DOI: https://doi.org/10.1007/978-3-642-02319-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02318-7
Online ISBN: 978-3-642-02319-4
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