Wind Power Prediction Interval Based on Predictive Density Estimation Within a New Hybrid Structure

Wind power prediction interval (WPPI) is the most common technique to represent wind power (WP) uncertainty. This article proposes a novel WPPI approach developed based on predictive density estimation (DE). Unlike most WPPI models in the literature, the proposed model does not need to solve a high-...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2022-12, Vol.18 (12), p.8563-8575
Main Authors: Rezaie, H., Chung, C. Y., Khorramdel, B.
Format: Article
Language:English
Subjects:
Density
Ear
Generalized cross-entropy (GCE)
hybrid deterministic probabilistic prediction
Hybrid structures
Informatics
Integrated circuits
optimal bandwidth (BW) selection
Optimization
prediction intervals (PIs)
Predictions
predictive density estimation (DE)
probabilistic prediction
Quadratic programming
Quantiles
Radio frequency
short-term wind power (WP) prediction
Task analysis
Tin
uncertainty representation
Wind forecasting
Wind power
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Summary:Wind power prediction interval (WPPI) is the most common technique to represent wind power (WP) uncertainty. This article proposes a novel WPPI approach developed based on predictive density estimation (DE). Unlike most WPPI models in the literature, the proposed model does not need to solve a high-dimensional optimization problem for model training. It optimizes the WPPIs using a single control variable-the bandwidth (BW) of DE-and trains the model directly and noniteratively using the quantiles extracted from the WP predictive density. For predictive DE, a novel application-specific method has been developed based on generalized cross-entropy (GCE). A precise but straightforward technique is designed to determine the optimal BW that results in the optimal WPPIs. The original GCE-based DE problem is also transformed into a convex quadratic programming formulation that can be solved quickly and uniquely. The WPPI model is employed in a new hybrid deterministic/probabilistic WPP (HDPWP) framework. Different from the conventional HDPWP approach that constructs WPPIs based on the point prediction error, the proposed framework incorporates WP point prediction among the predictor variables in the WPPI model, thereby improving performance. The effectiveness of the proposed methods is confirmed through extensive simulations and comparisons using real-world WP generation datasets.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3151798