风力发电
风电预测
风速
可靠性(半导体)
算法
电力系统
预测区间
残余物
区间(图论)
理论(学习稳定性)
计算机科学
功率(物理)
数学
工程类
气象学
机器学习
电气工程
物理
量子力学
组合数学
作者
Yagang Zhang,Xue Kong,Jingchao Wang,Hui Wang,Xiao-Dan Cheng
标识
DOI:10.1016/j.rser.2024.114349
摘要
Wind power generation has strong volatility. Accurate wind speed forecasting can not only avoid the waste of power resources, but also facilitate the development of clean energy and promote the energy transition worldwide. However, previous research has predominantly focused on the accuracy of wind power prediction, while ignoring the reliability of wind speed prediction system. In this research, a hybrid forecasting system with both accuracy and reliability of wind power forecasting is proposed. Firstly, a hybrid adaptive decomposition denoising algorithm is proposed to solve the unreasonable decomposition and residual noise. To improve the search performance, the seagull algorithm is optimized by chaotic system and Cauchy operator, and then the parameters of long short-term memory model are adjusted. Finally, based on data enhancement theory, an interval prediction model combined with kernel density estimation is proposed. The model is verified by the historical data of Sotavento wind farm in Spain and Eman wind farm in China. The average absolute percentage error values of wind speed point prediction are 2.87% and 8.01%, respectively. At the same confidence level, the interval prediction model proposed has narrower widths compared to the comparative model, with higher average interval scores. The results indicate that the point prediction model proposed in this research exhibits higher accuracy, while the interval prediction model demonstrates greater stability and reliability. These findings provide technical support for wind power forecasting.
科研通智能强力驱动
Strongly Powered by AbleSci AI