Short-term load forecasting using regression based moving windows with adjustable window-sizes

This paper presents a regression based moving window model for solving the short-term electricity forecasting problem. Moving window approach is employed to trace the demand pattern based on the past history of load and weather data. Regression equation is then formed and least square method is used to determine the parameters of the model. In this paper, a new concept associated with cooling and heating degree is used to establish the relationship between electricity demand and temperature, which is one of the key climatic variables. In addition, Pearson's correlation has been employed to investigate the interdependency of electricity demand between different time periods. These analyses together with the data in the holiday period provide the supportive information for the appropriate selection of the window size. A case study has been reported in this paper by acquiring the relevant data for the state of New South Wales, Australia. The results are then compared with a neural network based model. The comparison shows that the proposed moving window approach with the different window sizes outperforms conventional neural network technique in small time scales i.e., from 30 minutes to 1 day ahead.

[1]  S. A. Soliman,et al.  Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model , 2004 .

[2]  G. Harrison,et al.  Analyzing the Impact of Climate Change on Future Electricity Demand in Thailand , 2008, IEEE Transactions on Power Systems.

[3]  F. M. Andersen,et al.  Long term forecasting of hourly electricity consumption in local areas in Denmark , 2013 .

[4]  Kashem M. Muttaqi,et al.  Climate change impacts on electricity demand in the State of New South Wales, Australia , 2012 .

[5]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[6]  R. Campo,et al.  Adaptive Weather-Sensitive Short Term Load Forecast , 1987, IEEE Transactions on Power Systems.

[7]  M. O. Oliveira,et al.  Climate change effect on very short-term electric load forecasting , 2011, 2011 IEEE Trondheim PowerTech.

[8]  Shyh-Jier Huang,et al.  Short-term load forecasting via ARMA model identification including non-Gaussian process considerations , 2003 .

[9]  D. Sailor.  Relating residential and commercial sector electricity loads to climate : evaluating state level sensitivities and vulnerabilities , 2001 .

[10]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[11]  Naresh K. Sinha,et al.  Short-Term Load Demand Modeling and Forecasting: A Review , 1982, IEEE Transactions on Systems, Man, and Cybernetics.

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

[13]  R. Adapa,et al.  Risk due to load forecast uncertainty in short term power system planning , 1998 .

[14]  B. Hobbs,et al.  Analysis of the value for unit commitment of improved load forecasts , 1999 .

[15]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[16]  Ching-Lai Hor,et al.  Analyzing the impact of weather variables on monthly electricity demand , 2005, IEEE Transactions on Power Systems.

[17]  James W. Taylor,et al.  Triple seasonal methods for short-term electricity demand forecasting , 2010, Eur. J. Oper. Res..

[18]  Lisa Guan,et al.  Cross-correlations between weather variables in Australia , 2007 .

[19]  Linfield Brown,et al.  Statistics for Environmental Engineers , 2002 .

[20]  K. Srinivasan,et al.  Short term load forecasting using multiple correlation models , 1975, IEEE Transactions on Power Apparatus and Systems.

[21]  L. Suganthi,et al.  Energy models for demand forecasting—A review , 2012 .

[22]  G. Karady,et al.  Economic impact analysis of load forecasting , 1997 .