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2009 | 19 | 2 | 303-315
Tytuł artykułu

Ensemble neural network approach for accurate load forecasting in a power system

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.
Rocznik
Tom
19
Numer
2
Strony
303-315
Opis fizyczny
Daty
wydano
2009
otrzymano
2008-09-11
poprawiono
2009-01-17
Twórcy
  • Institute of the Theory of Electrical Engineering, Measurements and Information Systems, Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland
  • Institute of the Theory of Electrical Engineering, Measurements and Information Systems, Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland
  • Institute of Electronic Systems, Military University of Technology, ul. Kaliskiego 21, 00-908 Warsaw, Poland
  • Department of Business Informatics, Higher School of Economics, Al. Niepodległości 123, 02-554 Warsaw, Poland
Bibliografia
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  • Cichocki, A. and Amari, S. I. (2003). Adaptive Blind Signal and Image Processing, Wiley, New York, NY.
  • Cichocki, A., Amari, S., Siwek, K. and Tanaka, T. (2009). ICALAB Toolboxes, RIKEN, Tokyo, Available at: http://www.bsp.brain.riken.jp/ICALAB.
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  • Drezga, I. and Rahman, S. (1998). Input variable selection for ANN-based short-term load forecasting, IEEE Transactions on Power Systems 13(4): 1238-1244.
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  • Hippert, H. S., Pedreira, C. E. and Souza, R. C. (2001). Neural networks for short-term load forecasting: A review and evaluation, IEEE Transations on Power Systems 16(1): 44-55.
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  • Kiartzis, S. J., Zoumas, C. E., Theocharis, J., Bakirtzis, A. G. and Petridis, V. (1997). Short-term load forecasting in an autonomous power system using artificial neural networks, IEEE Transations on Power Systems 12(4): 1591-1596.
  • Kuntcheva, L. (2004). Combining Pattern Classifiers-Methods and Algorithms, Wiley, New York, NY.
  • Lendasse, A., Cottrell, M., Wertz, V. and Verleysen, M. (2002). Prediction of electric load using kohonen maps - Application to the Polish electricity consumption, Proceedings of the American Control Conference, Anchorage, AK, USA, Vol. 28, pp. 3684-3688.
  • Ljung, L. (1999). System Identification-Theory for the User, PTR Prentice Hall, Upper Saddle River, NJ.
  • Mandal, P., Senjyu, T., Urasaki, N. and Funabashi, T. (2006). A neural network based several hours ahead electric load forecasting using similar days approach, Electrical Power and Energy Systems 28(3): 367-373.
  • Nikias, L. and Petropulu, A. P. (1993). Higher-Order Spectral Analysis-A Nonlinear Signal Processing Framework, Prentice Hall, Upper Saddle River, NJ.
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  • Osowski, S. and Siwek, K. (1999). The self-organizing neural network approach to load forecasting in power system, Proceedings of the International Joint Conference on Neural Networks, Washington, DC, USA, Vol. 28, pp. 1345-1348.
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  • Sorjamaa, A., Hao, J., Reyhani, N., Li, Y. and Lendasse, A. (2007). Methodology for long-term prediction of time series, Neurocomputing 70(16-18): 2861-2869.
  • Yalcinoz, T. and Eminoglu, U. (2005). Short term and medium term power distribution load forecasting by neural networks, Energy Conversion and Management 46(8): 1393-1405.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv19i2p303bwm
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