Abstract
Electrical load forecasting is an important problem concerning safe and cost-efficient operation of the power system. Although many techniques are used to predict an electrical load, a research into constructing more accurate methods and software tools is still being conducted over the world. In this paper an experimental application for improving an accuracy of an electrical load prediction is presented. It is based on the syntactic pattern recognition approach and FGDPLL(k) string automata. The application has been tested on the real data delivered by one of the Polish electrical distribution companies.
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References
Alfares, H.K., Nazeeruddin, M.: Electric load forecasting: literature survey and classifcation of methods. Int. J. Syst. Sci. 33, 23–34 (2002)
Bunke, H.O., Sanfeliu, A. (eds.): Syntactic and Structural Pattern Recognition—Theory and Applications. World Scientific, Singapore (1990)
Flasiński, M., Jurek, J.: Dynamically programmed automata for quasi context sensitive languages as a tool for inference support in pattern recognition-based real-time control expert systems. Pattern Recognit. 32, 671–690 (1999)
Flasiński, M., Jurek, J.: On the analysis of fuzzy string patterns with the help of extended and stochastic GDPLL(k) grammars. Fundamenta Informaticae 71, 1–14 (2006) (IOS Press, Amsterdam)
Flasiński, M., Jurek, J.: Fundamental methodological issues of syntactic pattern recognition. Pattern Anal. Appl. 17, 465–480 (2014) (Springer, Berlin)
Flasiński, M., Jurek, J., Peszek, T.: Parallel Processing Model for Syntactic Pattern Recognition-Based Electrical Load Forecast. Lecture Notes in Computer Science, vol. 8384, pp. 338–347. Springer, Berlin (2014)
Fu, K.S.: Syntactic Pattern Recognition and Applications. Prentice Hall, Englewood Cliffs (1982)
Jurek, J.: Towards Grammatical Inferencing of GDPLL(k) Grammars for Applications in Syntactic Pattern Recognition-Based Expert Systems. Lecture Notes in Computer Science, vol. 3070, pp. 604–609. Springer, Berlin (2004)
Jurek, J.: Recent developments of the syntactic pattern recognition model based on quasi-context sensitive languages. Pattern Recognit. Lett. 26, 1011–1018 (2005) (Elsevier, Amsterdam)
Jurek, J.: Grammatical Inference as a Tool for Constructing Self-learning Syntactic Pattern Recognition-Based Agents. Lecture Notes in Computer Science, vol. 5103, pp. 712–721. Springer, Berlin (2008)
Jurek, J., Peszek, T.: Model of Syntactic Recognition of Distorted String Patterns with the Help of GDPLL(k)-Based Automata. Advances in Intelligent and Soft Computing, vol. 226, pp. 101–110. Springer, Berlin (2013)
Specht, D.F.: Probabilistic neural networks. Neural Netw. 3, 109–118 (1990)
Taylor, J., McSharry, P.: Short-term load forecasting methods: an evaluation based on European data. IEEE Trans. Power Syst. 22, 2213–2219 (2008)
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Flasiński, M., Jurek, J., Peszek, T. (2016). Application of Syntactic Pattern Recognition Methods for Electrical Load Forecasting. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_56
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DOI: https://doi.org/10.1007/978-3-319-26227-7_56
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