Mid-long-Term Regional Load Forecasting based on Cenzus X12-SARIMA Model

• Autorzy: Qiao Z.-J. , Li F.-I. , Li Y.

Warianty tytułu

PL

Przewidywanie obciążenia średnio-okresowego, na podstawie modelu Census X12_SARIMA

• Języki publikacji: EN

Abstrakty

EN

Regional power load time series has obviously trend circulation and seasonal cycle etc characteristics. In addition, using Census X12, such time series can be decomposed into trend circulation element, season element, irregular element etc. The paper attempts to establish a Census X12-SARIMA season adjustment model for mid-long-term regional power load analysis and prediction.Through empirical test for 92 months power load of Guangzhou and Suzhou area, 12 monthly power load from 2011.9 to 2012.8 was predicted. The results proved that Census X12SARIMA model is effective in mid-long-term regional power load analysis and prediction.

PL

W artykule podjęto próbę dopasowania modelu energetycznego Census X12-SARIMA na potrzeby średnio-okresowych analiz i predykcji obciążenia energetycznego. Na podstawie testów empirycznych, opartych na danych z 92 miesięcy obciążenia energetycznego regionów Guangshou i Suzhou, stworzony został 12 miesięczny profil– 09.2011-08.2012. Wyniki dowodzą ze Census X12-SARIMA jest efektywny w analizie średnio-okresowej.

Słowa kluczowe

EN

seasonal adjustment; Census X12-SARIMA model; load forecasting

PL

dopasowanie sezonowe; model Census X12-SARIMA; przewidywanie obciążenia

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 1b
• Strony: 224--227
• Opis fizyczny: Bibliogr. 9 poz., rys., tab.

Twórcy

autor

Qiao Z.-J.

• North China Institute of Science and Technology

autor

Li F.-I.

• North China Institute of Science and Technology

autor

Li Y.

• North China Institute of Science and Technology

Bibliografia

• [1] NIU Dong-xiao,CAO Shu-hua, ZHAO Lei. Power Load Forecast Technology and Application[M]. Beijing: China Electric Power Press, 1998,10.
• [2] Ghysels E, Lee H S, Noh J.Testing for unit roots in seasonal time series[J].Journal of Econometrics, 1994,(3):415~424
• [3] Hylleberg S. Modeling seasonal variation, in Hargreaves, C P. (ed.) Nonstationary time series analysis and co-integration [M]·Oxford: Oxford University Press,1994.54~66
• [4] THE PEOPLE'S BANK OF CHINA Statistics and Analysis Department: Time series X-12-ARIMA seasonal adjustment--- theory and method, [M], China's financial press, 2006.
• [5] Box G, Jenkins G, Reinsel C. Time Series Analysis: Forecasting and Control[M]. 3rd Edition. Englewood Cliffs, NJ: Prentice-Hall, 1994.
• [6] GAO Tiemei. The econometric analysis method and modeling -Eviews applications and examples[M]. Beijing:Tsinghua University press, 2006: 85~96.
• [7] Catherine C. Hood. Comparison of time series characteristics for seasonal adjustments from SEATS and X-12-ARIMA[J]. ASA proceedings, October, 2002.
• [8] U.S. Census Bureau. X-12ARIMA reference manual version 0·2·10,1~48[R].2002
• [9] Maraval A. and D.A. Pierce A prototypical seasonal adjustment model. Journal of Time Series Analysis, 8(1987)177-93