Gauss-Markov models for forecasting and risk evaluation

Author

Wong, Y.K. ; Rad, A.B.

Author_Institution

Dept. of Electr. Eng., Hong Kong Polytech., Hung Hom, Hong Kong

Volume

1

fYear

1998

fDate

3-5 Mar 1998

Firstpage

287

Abstract

In power systems, expansion of generating and transmission facilities, day-to-day operation are dependent on the future loading demands. Load uncertainty can be modeled using the Gauss-Markov properties for a random process. Sharing the Gauss-Markov characteristics, Box-Jenkins (ARIMA) forecast procedure is described. Then, electricity consumption data is simulated as an application of ARIMA models. Finally, a risk evaluation study using a Gauss-Markov load model is also demonstrated

Keywords

Markov processes; autoregressive moving average processes; load forecasting; power consumption; power systems; risk management; time series; ARIMA model; ARIMA models; Box-Jenkins forecast procedure; Gauss-Markov load model; Gauss-Markov models; autoregressive integrated moving average model; day-to-day operation; electricity consumption; future loading demands; generating facilities expansion; load forecasting; load uncertainty modelling; power systems; random process; risk evaluation; time series models; transmission facilities expansion; Autocorrelation; Gaussian processes; Load forecasting; Load modeling; Power generation; Power system modeling; Power system simulation; Predictive models; Random processes; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Energy Management and Power Delivery, 1998. Proceedings of EMPD '98. 1998 International Conference on

Print_ISBN

0-7803-4495-2

Type

conf

DOI

10.1109/EMPD.1998.705539

Filename

705539