Title :
Comparison of very short-term load forecasting techniques
Author :
Liu, K. ; Subbarayan, S. ; Shoults, R.R. ; Manry, M.T. ; Kwan, C. ; Lewis, F.L. ; Naccarino, J.
Author_Institution :
Autom. & Robotics Res. Inst., Texas Univ., Arlington, TX, USA
fDate :
5/1/1996 12:00:00 AM
Abstract :
Three practical techniques-fuzzy logic (FL), neural networks (NN), and autoregressive models-for very short-term power system load forecasting are proposed and discussed in this paper. Their performances are evaluated through a computer simulation study. The preliminary study shows that it is feasible to design a simple, satisfactory dynamic forecaster to predict very short-term power system load trends online. FL and NN can be good candidates for this application
Keywords :
autoregressive processes; fuzzy logic; load forecasting; neural nets; power system analysis computing; autoregressive models; computer simulation; fuzzy logic; neural networks; performance evaluation; power systems; very short-term load forecasting; Application software; Computer simulation; Load forecasting; Logic; Neural networks; Performance evaluation; Power system dynamics; Power system modeling; Power system simulation; Predictive models;
Journal_Title :
Power Systems, IEEE Transactions on
