PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Short Term Load Forecasting Based on WLS-SVR and TGARCH Error Correction Model in Smart Grid

Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
PL
Przewidywanie krótkoterminowe obciążenia inteligentnej sieci elektroenergetycznej z wykorzystaniem modelu WLS-SVR oraz korekcji błędów modelem TGARCH
Języki publikacji
EN
Abstrakty
EN
Smart grid is the main development goal of future power grid while the short-term load forecasting is the significant premise of making management, power supply and trading plan in market circumstance. The forecasting accuracy directly determined the safety and economy of electric system. Support Vector Machines (SVM), as the new machine learning method, has applied successfully to short-termed load forecasting. However, research finds out that the singular points of the initial data have impact on forecasting accuracy. So in this paper, firstly, based on the analysis of SVM, we render Weighted Least Square and Support Vector Regression (WLS-SVR) applying to short-termed load forecasting, which overcomes the disadvantage of singular points. Secondly, we offer Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) model to construct error prediction model to modify the initial predicted value. Finally, according to the PJM historical data, we get the results showing that the accuracy is greatly improving by implementing our methods which makes our methods founded.
PL
W artykule przedstawiono model przewidywania krótkookresowego obciążenia sieci elektroenergetycznej. W proponowanym rozwiązaniu wykorzystano metodę SVM (ang. Support Vector Machine). W celu eliminacji istniejącego wpływu wartości syngularnych na dokładność wyniku, zastosowano regresję ze średnią ważoną. Dodatkowo wykorzystano model TGARCH w określaniu błędów predykcji. Przedstawiono wyniki badań weryfikacyjnych, przeprowadzonych na rzeczywistych danych.
Rocznik
Strony
170--175
Opis fizyczny
Bibliogr. 24 poz., schem., tab., wykr.
Twórcy
autor
  • School of Management, Hefei University of Technology, Anhui Hefei 230009 P. R. China
autor
  • School of Management, Hefei University of Technology, Anhui Hefei 230009 P. R. China
autor
  • School of Management, Hefei University of Technology, Anhui Hefei 230009 P. R. China
autor
  • School of Management, Hefei University of Technology, Anhui Hefei 230009 P. R. China
Bibliografia
  • [1] HAASE P. IntelliGrid: a smart network of power [EB/OL]. [2008-0721]. http://mydocs.epri.com/docs/CorporateDocuments/EPRI_Journal/2005-Fall/1012885_IntelliGrid.pdf
  • [2] U.S. Department of Energy. Grid2030: a national vision for electricity’s second 100 years [EB/OL]. [2008-07-21]. http://www.oe.energy.gov/Documents and Media/Electric_Vision Document.pdf.
  • [3] The Grid Wise Alliance. Gridwise action plan [EB/OL]. [2008-04-01]. http://www.gridwise. org/pdf/actionplan.pdf.
  • [4] European Commission. European technology platform smart Grids: vision and strategy for Europe’s electricity networks of the future [EB/OL]. [2008-07-21]. http://www. smart grids.eu/documents/vision.pdf.
  • [5] Aldo Goia, Caterina May, Gianluca Fusai, Functional clustering and linear regression for peak load forecasting, International Journal of Forecasting, 22 (2010), 1-11.
  • [6] Sp.Pappas, L.Ekonomuou, P.Karampelas, Electricity demand load forecasting of the Hellenic power system using an ARMA model, Electric Power Systems Research, 80(2010), 256-264.
  • [7] J.F.Liu, Z.L.Deng, Self-Tuning Weighted Measurement Fusion Kalman Filter for ARMA Signals with Colored Noise, Applied Mathematics & Information Sciences, 6 (2012), 1-7.
  • [8] Nima Amjady, Farshid Keynia, A New Neural Network Approach to Short Term Load Forecasting of Electrical Power System, Energies, 4(2011),488-503.
  • [9] P A Mastorocostas, J B Theocharis, Vassilios S Petridis, A constrained orthogonal east squares method for generating TSK fuzzy models: Application to short-term load forecasting, Fuzzy Sets and Systems, 118(2001), 215-233.
  • [10] Cortes C, Vapnik V. Support-vector Networks, Machine Learning,20(1995),273-297.
  • [11] Suykems J A K, Validewalle J, Least squares support vector machine classifiers, Neural processing letters,9(1999),293-300.
  • [12] M.Zhou, Z.Yan, et al, A novel ARIMA approach on electricity price forecasting with the improvement of predicted error, Proceedings of the CSEE, 6(2004),63-68.
  • [13] W.M.Liu,K,Yang, Day-ahead Electricity Price Forecasting with Error Calibration by Hidden Markov Model, Automation of Electric Power Systems, 4(2009),34-37.
  • [14] Nelson, D.B., Cao, C.Q., Inequality constraints in the univariate GARCH model, Journal of Business and Economic Statistics, 10(1992), 229–235.
  • [15] Comte, F., Lieberman, O., Asymptotic theory for multivariate GARCH processes, Journal of Multivariate Analysis, forthcoming (2000).
  • [16] Lee, J., King, M., A locally most mean powerful based score test for ARCH and GARCH regression disturbances, Journal of Business and Economic Statistics, 11(1993), 17–27.
  • [17] Demos, A., Sentana, E., Testing for GARCH : a one-sided approach Journal of Econometrics, 86(1998), 97–127.
  • [18] Henrique S, James W. Taylor, An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-load forecasting, Neural Networks, 5(2010),386-395.
  • [19] J.Z.Wang, S.L. Zhu et al, Combined modelling for electric load forecasting with adaptive particle swarm optimization,Energy, (2010),1671-1678.
  • [20] Diyar Akay, Mehmet Atak, Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, (2007),1670-1675.
  • [21] Ehab E. Elattar, John Goulermas,et al, Electric Load Forecasting Based on Locally. Weighted Support Vector Regression. IEEE Transactions on systems, (2010),438-447.
  • [22] P.F. Pai, K.P. Lin et al, Time series forecasting by a seasonal support vector, regression model, Expert Systems with Applications, (2010),4261-4265.
  • [23] Henrique S, James W. Taylor An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-load forecasting, Neural Networks, (2010),386-395.
  • [24] Diyar Akay, Mehmet AtakGrey prediction with rolling mechanism for electricity demand forecasting of Turkey . Energy, (2007),1670-1675.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-75a551b9-a799-466c-b31b-7538cb9404ef
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.