A Nonparametric Probability Distribution Model for Short-Term Wind Power Prediction Error

Accurate wind power prediction error (WPPE) modeling is of high importance in power systems with large scale wind power generation containing high level of uncertainty. Since WPPE cannot be entirely removed, providing its accurate probability distribution model can assist power system operators in mitigating its negative effects on decision making conditions. In this paper, unlike previous related works, a nonparametric model is presented using kernel density estimation (KDE) with an efficient bandwidth (BW) selection technique called “advanced plug-in” technique. The utilized BW selection technique enables KDE to accurately estimate important features of WPPE distribution, e.g., fat tails, high skewness and kurtosis. The proposed WPPE modeling approach is simulated using one-year time series of real wind power and corresponding predicted values for 1-hour look-ahead time. The efficacy of the proposed WPPE model is depicted using Centennial wind farm dataset in south of Saskatchewan province in Canada. Results show that parametric distribution models like Normal, Stable, and so on may not properly model the uncertainty of WPPE.