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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-05e40d12-1cb3-448d-aac7-b50c3ac5cf9a

Czasopismo

Przegląd Elektrotechniczny

Tytuł artykułu

The Effect of System Characteristics on Very-Short-Term Load Forecasting

Autorzy Katzir, L.  Loewenstern, Y.  Shmilovitz, D. 
Treść / Zawartość http://pe.org.pl/
Warianty tytułu
PL Odziaływanie cech systemu na krótkoterminowe przewidywanie obciążenia
Języki publikacji EN
Abstrakty
EN The rise of the Smart Grid and Microgrid concepts require load demand control at short lead times, at a resolution of minutes, leading to the need for Very Short Term Load Forecasting (VSTLF). This study builds upon previous research of load forecast and investigates the relationship between system characteristics and the achievable of VSTLF accuracy. The results presented here are based on study and simulated forecasting of three years’ worth of real load data obtained from the New York Independent System Operator (NYISO).
PL Koncepcje Sieci Inteligentnych oraz MicroSieci wymagają sterowania z krótkim czasem wyprzedzania, rzędu minut, co prowadzi do zapotrzebowania na Bardzo Krótko Terminowe Przewidywanie Obciążenia (ang.: Very Short Term Load Forecasting - VSTLF). Przedstawione badnia są kontynuacją poprzednich nad przewidywaniem obciążenia i dotyczą związku między cechami systemu i osiągalną dokładnością VSTLF. Przedstawione wyniki są oparte na badaniu oraz na modelowaniu trzyletniego przewidywania obciążenia rzeczywistego, na podstawie danych otrzymanych od New York Independent System Operator (NYISO).
Słowa kluczowe
PL przewidywanie obciążenia   modelowanie obciążenia   system energetyczny   sieć inteligentna  
EN load forecasting   Load Modelling   power system   smart grid  
Wydawca Wydawnictwo SIGMA-NOT
Czasopismo Przegląd Elektrotechniczny
Rocznik 2015
Tom R. 91, nr 11
Strony 119--123
Opis fizyczny Bibliogr. 22 poz., rys., tab., wykr.
Twórcy
autor Katzir, L.
autor Loewenstern, Y.
autor Shmilovitz, D.
Bibliografia
[1] H.S. Hippert, C.E. Pedreira and R.C. Souza, ”Neural Networks for Short- Term Load Forecasting: A Review and Evaluation”, IEEE Transactions on Power Systems, Vol. 16, No. 1, pp. 44-53, February 2001.
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[5] K. Liu, S. Subbarayan, R.R. Shoults, M.T. Manry, C. Kwan, F.L. Lewis and J. Naccarino, ”Comparison of very short-term load forecasting techniques,” IEEE Transactions on Power Systems, vol.11, no.2, pp.877,882, May 1996
[6] W.C. Hong, ”Electric load forecasting by support vector model”, Applied Mathematical Modeling, Vol. 33, pp. 24442454, 2009.
[7] E.A. Feinberg, and D. Genethliou, ”Load Forecasting”, Applied Mathematics for Power Systems, pp. 269-285, Springer, 2005
[8] A. Khosravi and Saeid Nahavandi, ”Load Forecasting Using Interval Type-2 Fuzzy Logic Systems: Optimal Type Reduction”, IEEE Transactions on Industrial Informatics, vol. 10., no. 2, pp. 1055-1063, May 2014
[9] Y. Loewenstern, L. Katzir and D. Shmilovitz, ”Statistical Analysis of Power Systems and Application to Load Forecasting”, Proceeding of the IEEE 28-th Convention of Electrical and Electronics Engineers in Israel, Eilat, 2014
[10] www.nyiso.com
[11] C.E Borges, Y.K. Penya, I. Fernandez, ”Evaluating Combined Load Forecasting in Large Power Systems and Smart Grids”, IEEE Transactions on Industrial Informatics, vol. 9., no. 3, pp. 1570-1577, August 2013
[12] W. Labeeuw, G. Deconinck ”Residential Electrical Load ModelBased on Mixture Model Clustering and Markov Models”, IEEE Transactions on Industrial Informatics, vol. 9., no. 3, pp. 1561-1569, August 2013
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[14] W. Charytoniuk and M.S. Chen, ”Very Short-Term Load Forecasting Using Artificial Neural Networks”, IEEE Transactions on Power Systems, vol. 15, no. 1, pp. 263-268, February 2000.
[15] E.A. Blood, M.D. Ilic, B.H. Krogh, ”A Kalman Filter Approach to Quasi-Static State Estimation in Electric Power Systems,” 38th North American Power Symposium, NAPS 2006, pp. 417-422, September 2006
[16] A.K. Sinha, ”Short term load forecasting using artificial neural networks,” Proceedings of IEEE International Conference on Industrial Technology, vol.1, pp.548-553, January 2000
[17] J.W. Taylor and P.E. McSharry, ”Short-Term Load Forecasting Methods: An Evaluation Based on European Data”, IEEE Transactions on Power Systems, vol. 22, no. 4, pp. 2213-2219, November 2007.
[18] G.E.P. Box, G.M. Jenkins and G.C. Reinsel, ”Time Series Analysis” (3rd edition), Prentice-Hall, Inc., 1994.
[19] P.R. Winters, ”Forecasting sales by exponentially weighted moving averages”, Mngt Sci no. 6, pp 324-342, 1960.
[20] C.C. Holt, ”Forecasting seasonals and trends by exponentially weighted moving averages”, International Journal of Forecasting, Volume 20, Issue 1, March 2004, pp. 5-10
[21] A.M. Leite da Silva, M.B. Do Coutto Filho and J.F.de Queiroz, ”State forecasting in electric power systems”, IEE Proceedings C on Generation, Transmission and Distribution, vol.130, no.5, pp.237,244, September 1983
[22] J.W. Taylor, ”Short-term electricity demand forecasting using double seasonal exponential smoothing”, Journal of the Operational Research Society, no. 54, pp. 799-805, 2003.
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