Hour-Ahead Wind Power Prediction for Power Systems using Hidden Markov Models and Viterbi Algorithm

被引：0

作者：

Jafarzadeh, Saeed ^{[1
]}

Fadali, Sami ^{[1
]}

Evrenosoglu, Cansin Yaman ^{[1
]}

Livani, Hanif ^{[1
]}

机构：

[1] Univ Nevada, Elect & Biomed Engn Dept, Reno, NV 89557 USA

来源：

IEEE POWER AND ENERGY SOCIETY GENERAL MEETING 2010 | 2010年

关键词：

wind power prediction; statistical analysis; power system operation; Viterbi; Hidden Markov Model; wind forecasting; SHORT-TERM PREDICTION; SPEED; GENERATION; INTEGRATION; ENERGY;

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：

This paper presents a new stochastic method for very short-term (1 hour) wind prediction in electrical power systems. The method utilizes Hidden Markov Models (HMM) and the Viterbi Algorithm (VA). Past wind farm power production data are required to develop the HMM model. The accuracy of the predictions improves drastically if hourly weather forecast data are used as pseudo-measurements. Computer simulations using Northwestern weather recordings from the Bonneville Power Administration (BPA) website show good correlation between our predictions and the actual data.

引用

页数：6

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