Autoregressive method in short term load forecast

Author

Baharudin, Zuhairi ; Kamel, Nidal

Author_Institution

Electr. & Electron. Dept., Univ. Teknol. Petronas, Tronoh

fYear

2008

fDate

1-3 Dec. 2008

Firstpage

1603

Lastpage

1608

Abstract

Short-term load forecasting plays an important role in planning and operation of power system.The accuracy of this forecasted value is necessary for economically efficient operation and also for effective control.This paper describes the methods of autoregressive (AR) Burgpsilas and modified covariance (MCOV) in solving a short term load forecast.The methods are tested based on historical load data of New South Wales, Australia.The accuracy of discussed methods are obtained and reported.

Keywords

autoregressive moving average processes; covariance analysis; load forecasting; power system control; power system planning; Australia; Burgs method; New South Wales; autoregressive moving average method; covariance analysis; power system control; power system operation; power system planning; short-term load forecast; Artificial intelligence; Artificial neural networks; Autoregressive processes; Demand forecasting; Economic forecasting; Fuzzy logic; Load forecasting; Power generation economics; Power system planning; Weather forecasting; ANN; Autoregressive moving average (ARMA); Burg; MAPE; MCOV; Short term load forecasting (STLF); autoregressive (AR);

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International

Conference_Location

Johor Bahru

Print_ISBN

978-1-4244-2404-7

Electronic_ISBN

978-1-4244-2405-4

Type

conf

DOI

10.1109/PECON.2008.4762735

Filename

4762735