Autoregressive method in short term load forecast

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.

[1]  Kyung-Bin Song,et al.  Hybrid load forecasting method with analysis of temperature sensitivities , 2006, IEEE Transactions on Power Systems.

[2]  James Durbin,et al.  The fitting of time series models , 1960 .

[3]  T. Funabashi,et al.  One-Hour-Ahead Load Forecasting Using Neural Networks , 2002 .

[4]  W. Charytoniuk,et al.  Very short-term load forecasting using artificial neural networks , 2000 .

[5]  S. M. El-Debeiky,et al.  Long-Term Load Forecasting for Fast-Developing Utility Using a Knowledge-Based Expert System , 2002, IEEE Power Engineering Review.

[6]  Hak-Keung Lam,et al.  Short-term electric load forecasting based on a neural fuzzy network , 2003, IEEE Trans. Ind. Electron..

[7]  Alireza Khotanzad,et al.  A Neuro-Fuzzy Approach to Short-Term Load Forecasting in a Price-Sensitive Environment , 2002, IEEE Power Engineering Review.

[8]  J. Cadzow.  Maximum Entropy Spectral Analysis , 2006 .

[9]  Chao-Ming Huang,et al.  A particle swarm optimization to identifying the ARMAX model for short-term load forecasting , 2005 .

[10]  Ming-Wei Chang,et al.  Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 , 2004, IEEE Transactions on Power Systems.

[11]  Vladimiro Miranda,et al.  Fuzzy inference in spatial load forecasting , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[12]  Nima Amjady,et al.  Short-term hourly load forecasting using time-series modeling with peak load estimation capability , 2001 .

[13]  Qia Ding,et al.  Long-Term Load Forecast Using Decision Tree Method , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[14]  R.-H. Liang,et al.  Combined regression-fuzzy approach for short-term load forecasting , 2000 .

[15]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[16]  S. Soliman,et al.  Fuzzy short-term electric load forecasting using Kalman filter , 2006 .

[17]  Mihai Gavrilas,et al.  Medium-term load forecasting with artificial neural network models , 2001 .

[18]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[19]  D. Asber,et al.  Modeling of Distribution Loads for Short and Medium-Term Load Forecasting , 2007, 2007 IEEE Power Engineering Society General Meeting.

[20]  G. Gross,et al.  Short-term load forecasting , 1987, Proceedings of the IEEE.

[21]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[22]  R. Buizza,et al.  Neural Network Load Forecasting with Weather Ensemble Predictions , 2002, IEEE Power Engineering Review.

[23]  S. J. Kiartzis,et al.  A fuzzy expert system for peak load forecasting. Application to the Greek power system , 2000, 2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099).

[24]  Avelino J. Gonzalez,et al.  Short-term electrical load forecasting using a fuzzy ARTMAP neural network , 1998, Defense, Security, and Sensing.

[25]  S. A. Villalba,et al.  Hybrid demand model for load estimation and short term load forecasting in distribution electric systems , 2000 .

[26]  M. M. Elkateb,et al.  Hybrid adaptive techniques for electric-load forecast using ANN and ARIMA , 2000 .

[27]  S. Fan,et al.  Short-term load forecasting based on an adaptive hybrid method , 2006, IEEE Transactions on Power Systems.

[28]  C. N. Lu,et al.  A Data Mining Approach for Spatial Modeling in Small Area Load Forecast , 2002, IEEE Power Engineering Review.

[29]  Charles W. Therrien,et al.  Discrete Random Signals and Statistical Signal Processing , 1992 .