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An Advanced Statistical Method for Wind Power Forecasting

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2 Author(s)
George Sideratos ; Nat. Tech. Univ. of Athens ; Nikos D. Hatziargyriou

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

Published in:

IEEE Transactions on Power Systems  (Volume:22 ,  Issue: 1 )