4.6 Article

An Enhanced Performance Evaluation Metrics for Wind Power Ramp Event Forecasting

Journal

IEEE ACCESS
Volume 11, Issue -, Pages 100195-100206

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3313632

Keywords

Forecasting; Wind power generation; Predictive models; Logic gates; Measurement; Wind forecasting; Mathematical models; Performance evaluation; Event detection; Performance evaluation metric; power system security; ramp event forecasting; wind power

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This study proposed an enhanced performance evaluation metric for wind power ramp event forecasting, and analyzed the forecasting results using this metric. The results highlight the advantages of the proposed metric over the widely used confusion matrix for performance evaluation, which has more detailed and visually based analytical capabilities. The study is important for real-time curtailment forecasting and decision-making in high wind power scenarios.
As wind power is volatile and intermittent, an increase in the proportion of wind power generation may affect the security of power systems. Accurate wind power generation and ramp forecasting are crucial for reducing operating costs and maintaining the power balance. This study proposed an enhanced performance evaluation metric for wind power ramp event forecasting, and analyzed the forecasting results using this metric. Experimental ramp event forecasting was conducted on wind farms located in Jeju, South Korea by employing an indirect forecasting method for wind power output forecasting. Considering the evaluation of forecasting performance, the accuracy (ACC) was observed to be 0.86 for Case #1 and 0.90 for Case #2. The results highlight the advantages of the proposed metric over the widely used confusion matrix for performance evaluation, which has more detailed and visually based analytical capabilities. The simulation demonstrates that the proposed approach is poised to make a noteworthy contribution in the advancement of tool development, enabling real-time curtailment forecasting and facilitating well-informed decision-making in high wind power scenarios.

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