@inproceedings{adc29d6f187d4a72a96f6f49d40541ae,
title = "A short-term wind power probability prediction method based on soft clustering and similarity measurement",
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.",
keywords = "Wind power prediction, interval prediction, similarity measurement, soft clustering",
author = "Zhiwei Liu and Xin Liu and Lin Gong and Minxia Liu and Xi Xiang and Jian Xie and Yongyang Zhang",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 4th International Conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2024 ; Conference date: 26-01-2024 Through 28-01-2024",
year = "2024",
doi = "10.1117/12.3030457",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Khan, {Zeashan Hameed} and Junxing Zhang and Pengfei Zeng",
booktitle = "Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2024",
address = "United States",
}