风电预测
风力发电
极限学习机
极端天气
概率逻辑
期限(时间)
气象学
时间序列
概率预测
风速
电力系统
计算机科学
环境科学
功率(物理)
工程类
人工智能
机器学习
地理
气候变化
人工神经网络
生物
生态学
物理
量子力学
电气工程
作者
Guang Zheng Yu,Lu Liu,Bo Tang,Si Yuan Wang,C. Y. Chung
出处
期刊:IEEE Transactions on Power Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-12-12
卷期号:38 (6): 5045-5056
被引量:18
标识
DOI:10.1109/tpwrs.2022.3224557
摘要
Extreme weather events have become more frequent in recent years. Wind power can fluctuate violently in a short period of time due to the influence of extreme weather, which creates challenges with respect to ultra-short-term wind power forecasting. Thus, this article proposes an ultra-short-term wind power subsection forecasting method based on extreme weather identification. A power time series trend discrimination method and an inflection point (IP) detection method are proposed to accurately identify extreme weather periods (EWPs). Feature recognition is carried out for power time series with multiple weather models. Finally, a method combining both improved gated recurrent unit (GRU) point forecasting and improved kernel density estimation-wind power probabilistic forecasting is developed. Wind farm data from Texas, USA are used to verify the predictive performance, and the results show the method effectively improves accuracy.
科研通智能强力驱动
Strongly Powered by AbleSci AI