Short-Term Electricity Price Forecasting

被引：0

作者：

Arabali, A. ^{[1
]}

Chalko, E. ^{[1
]}

Etezadi-Amoli, M. ^{[1
]}

Fadali, M. S. ^{[1
]}

机构：

[1] Univ Nevada, EE Dept, Reno, NV 89557 USA

来源：

2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES) | 2013年

关键词：

electricity price; Kalman filter; H-infinity filter; Gauss-Markov;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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-infinity 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-infinity filter are generally superior to those for the standard Kalman filter.

引用

页数：5

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