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

• Autorzy: Hou L. , Yang S. , Wang X. , Shen J.

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.

Słowa kluczowe

EN

smart grid; TGARCH; error correction; WLS-SVR; short-term load forecasting

PL

inteligentne sieci; TGARCH; korekcja błędu; WLS-SVR; przewidywanie krótkoterminowe obciążenia

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 3b
• Strony: 170--175
• Opis fizyczny: Bibliogr. 24 poz., schem., tab., wykr.

Twórcy

autor

Hou L.

• School of Management, Hefei University of Technology, Anhui Hefei 230009 P. R. China

autor

Yang S.

• School of Management, Hefei University of Technology, Anhui Hefei 230009 P. R. China

autor

Wang X.

• School of Management, Hefei University of Technology, Anhui Hefei 230009 P. R. China

autor

Shen J.

• School of Management, Hefei University of Technology, Anhui Hefei 230009 P. R. China

Bibliografia

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