Short-term electricity price forecasting

Author

Arabali, A. ; Chalko, E. ; Etezadi-Amoli, M. ; Fadali, Mohammed Sami

Author_Institution

EE Dept., Univ. of Nevada, Reno, NV, USA

fYear

2013

fDate

21-25 July 2013

Firstpage

1

Lastpage

5

Abstract

Price forecasting has become an important tool in the planning and operation of restructured power systems. This paper develops a new short-term electricity price forecasting scheme based on a state space model of the power market. A Gauss-Markov process is used to represent the stochastic dynamics of the electricity market. Kalman and H_∞ filters, two methods based on the state space model, are applied in order to estimate the electricity price and compare the quality of their state estimates. Our results show that performance measures for the H_∞ filter are generally superior to those for the standard Kalman filter.

Keywords

Gaussian processes; Kalman filters; Markov processes; power markets; pricing; Gauss Markov process; Kalman filters; electricity market; power market; restructured power systems; short term electricity price forecasting; state space model; stochastic dynamics; Electricity; Equations; Forecasting; Kalman filters; Mathematical model; Noise; Power markets; Gauss-Markov; H∞ filter; Kalman filter; electricity price;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Society General Meeting (PES), 2013 IEEE

Conference_Location

Vancouver, BC

ISSN

1944-9925

Type

conf

DOI

10.1109/PESMG.2013.6672910

Filename

6672910