A Fuzzy Adaptive Probabilistic Wind Power Prediction Framework Using Diffusion Kernel Density Estimators

• Published in: IEEE transactions on power systems Vol. 33; no. 6; pp. 7109 - 7121
• Main Authors: Khorramdel, Benyamin, Chung, C. Y., Safari, Nima, Price, G. C. D.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer simulation; Computing time; Density; Electric power generation; Estimation; Fuzzy systems; Kernel; Kernel density estimation; Power efficiency; prediction intervals; Predictive control; Probabilistic logic; probabilistic wind power prediction; Time series; Time series analysis; Uncertainty; Wind power; Wind power generation; wind power time series
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2018.2848207

The inherent uncertainty in predicting wind power generation makes the operation and control of power systems very challenging. Probabilistic measurement of wind power uncertainty in the form of a reliable and sharp interval is of utmost importance, but construction of such high-quality prediction intervals (PIs) is difficult because wind power time series are nonstationary. In this paper, a framework based on the concept of bandwidth selection for a new and flexible kernel density estimator is proposed. Unlike previous related works, the proposed framework uses neither a cost function-based optimization problem nor point prediction results; rather, a diffusion-based kernel density estimator (DiE) is utilized to achieve high-quality PIs for nonstationary wind power time series. Moreover, to adaptively capture the uncertainties of both the prediction model and wind power time series in different seasons, the DiE is equipped with a fuzzy inference system and a tri-level adaptation function. The proposed framework is also founded based on a parallel computing procedure to promote the computational efficiency for practical applications in power systems. Simulation results demonstrate the efficiency of the proposed framework compared to well-known conventional benchmarks using real wind power datasets from Canada and Spain.