An Advanced Statistical Method for Wind Power Forecasting

Author

Sideratos, George ; Hatziargyriou, Nikos D.

Author_Institution

Nat. Tech. Univ. of Athens

Volume

22

Issue

1

fYear

2007

Firstpage

258

Lastpage

265

Abstract

This paper presents an advanced statistical method for wind power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of wind speed and direction interpolated at the site of the wind farm. A self-organized map is trained to classify the forecasted local wind speed provided by the meteorological services. A unique feature of the method is that following a preliminary wind power prediction, it provides an estimation of the quality of the meteorological forecasts that is subsequently used to improve predictions. The proposed method is suitable for operational planning of power systems with increased wind power penetration, i.e., forecasting horizon of 48 h ahead and for wind farm operators trading in electricity markets. Application of the forecasting method on the power production of an actual wind farm shows the validity of the method

Keywords

artificial intelligence; load forecasting; power engineering computing; power generation planning; self-organizing feature maps; wind power plants; artificial intelligence techniques; meteorological forecasting; operational planning; power measurements; quality estimation; self-organized map; statistical method; wind farm; wind power forecasting; wind speed; Artificial intelligence; Economic forecasting; Meteorology; Power system planning; Statistical analysis; Weather forecasting; Wind energy; Wind farms; Wind forecasting; Wind speed; Fuzzy sets; radial base function networks; self-organized feature maps; wind power forecasting;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/TPWRS.2006.889078

Filename

4077140