A short-term wind power probability prediction method based on soft clustering and similarity measurement

Zhiwei Liu; Xin Liu; Lin Gong; Minxia Liu; Xi Xiang; Jian Xie; Yongyang Zhang

doi:10.1117/12.3030457

5 June 2024 A short-term wind power probability prediction method based on soft clustering and similarity measurement

Zhiwei Liu, Xin Liu, Lin Gong, Minxia Liu, Xi Xiang, Jian Xie, Yongyang Zhang

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131633P (2024) https://doi.org/10.1117/12.3030457
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

With the rapid development of wind energy, probabilistic forecasting of wind power becomes increasingly crucial for reliable operations of power grids. This paper proposes a wind power interval prediction method based on temporal data soft clustering and similarity measurement (SCSM). First, a soft clustering module is used to cluster wind power data with probabilities. Next, a similarity measurement module assesses the similarity between wind power data based on soft clustering results and generates probability interval predictions by referring to historical prediction errors. Finally, the effectiveness of the proposed method is validated using real wind power data.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhiwei Liu, Xin Liu, Lin Gong, Minxia Liu, Xi Xiang, Jian Xie, and Yongyang Zhang "A short-term wind power probability prediction method based on soft clustering and similarity measurement", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131633P (5 June 2024); https://doi.org/10.1117/12.3030457

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