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Przegląd metod wykorzystywanych do średnioterminowego prognozowania obciążeń elektroenergetycznych

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Warianty tytułu
EN
Medium-term load forecasting models - a review
Języki publikacji
PL
Abstrakty
PL
W artykule dokonano przeglądu metod i modeli prognostycznych dedykowanych średnioterminowemu prognozowaniu obciążeń elektroenergetycznych. Opisano metody modelowania warunkowego i autonomicznego, modele klasyczne, modele inteligencji obliczeniowej i uczenia maszynowego oraz modele oparte na podobieństwie obrazów.
EN
The article reviews the methods and models of the medium-term load forecasting. Methods of conditional and autonomous modeling, classic models, computational intelligence and machine learning models are described, as well as pattern similarity-based models.
Rocznik
Strony
155--159
Opis fizyczny
Bibliogr. 41 poz.
Twórcy
  • Politechnika Częstochowska, Instytut Informatyki, al. Armii Krajowej 17, 42-200 Częstochowa
Bibliografia
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  • [10] Dudek G.: Analiza podobieństwa obrazów sekwencji szeregów czasowych obciążeń elektroenergetycznych, Przegląd Elektrotechniczny 85 (2009), nr 3, 149-152
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  • [26] Dong-Liang Z., Yanjian, Wei-Hua W., Xiu-Lan Y. : Mid-long term load forecasting of the unstable growth sequence based on Markov chains screening combination forecasting models, 2016 China International Conference on Electricity Distribution (CICED 2016), Xi’an, 10-13 Aug, 2016
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  • [31] González-Romera E., Jaramillo-Morán M.A., Carmona-Fernández D.: Monthly electric energy demand forecasting based on trend extraction. IEEE Trans. Power System, 21 (2006), nr 4,1935–46
  • [32] Chen J.F., Lo S.K., Do Q.H.: Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained byHeuristic Algorithms, Information, 31 (2017), nr 8
  • [33] Aquinode R. R. B, Neto O. N., Lira M. M. S., Ferreira A. A., Carvalho Jr. M.A., Silva G. B., Oliveirade J. B.: Development of an Artificial Neural Network by Genetic Algorithm to Mid-Term Load Forecasting, Proceedings of 2007 International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12-17, 2007
  • [34] Borlea I ., Buta A., Lustrea B.: Some Aspects Concerning Mid Term Monthly Load Forecasting Using ANN, EUROCON 2005 - The International Conference on "Computer as a Tool", Belgrade, November 22-24, 2005
  • [35] Zhao W., Wang F., Niu D. : The Application of Support Vector Machine in Load Forecasting, JOURNAL OF COMPUTERS, 7 (2012), nr. 7, 1615-1622
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  • [38] Dudek G., Pełka P.: Forecasting monthly electricity demand using k nearest neighbor method, Przegląd Elektrotechniczny, 93 (2017) ,nr.4, 62-65.
  • [39] Pełka P. , Dudek G.: Prediction of Monthly Electric Energy Consumption using Pattern-Based Fuzzy Nearest Neighbour Regression. Proc. 2nd Int. Conf. Computational Methods in Engineering Science (CMES'17), ITM Web Conf., 15 (2017), 1-5
  • [40] Dudek G., Pełka P.: Medium-term electric energy demand forecasting using Nadaraya-Watson estimator. Proc. 18th Int. Scientific Conf. on Electric Power Engineering 2017 (EPE'17), 1-6
  • [41] Pełka P., Dudek G.: Neuro-Fuzzy System for Medium-term Electric Energy Demand Forecasting. In: Borzemski L., Świątek J., Wilimowska Z. (eds) Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017, Advances in Intelligent Systems and Computing, Springer, Cham, 655 (2018), 38-47
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bbe3b58e-9f71-4ae3-94d5-9396ff4d946e
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