一个新的生物灵感优化算法设计,用于风力发电工程应用程序的时间序列预测
Faten Khalid Karim1, Doaa Sami Khafaga1, Marwa M Eid2
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Biomimetics (Basel, Switzerland)
|July 28, 2023
概括
气候变化影响风力模式,影响风力发电的可预测性. 一个新的循环神经网络 (RNN) 模型与动态适应性Al-Biruni地球半径 (DFBER) 算法显示出优越的风力发电预测性能.
科学领域:
- 可再生能源系统可再生能源系统
- 适应气候变化 适应气候变化
- 人工智能在能源中的作用
背景情况:
- 气候变化正在改变风力模式,导致不可预测的风力发电.
- 准确的风力发电预测对于电网稳定性和高效的能源管理至关重要.
- 现有的预测模型与风格的动态性质作斗争.
研究的目的:
- 提出一种与动态健身Al-Biruni地球半径 (DFBER) 算法集成的新型反复神经网络 (RNN) 预测模型.
- 提高风力发电数据模式预测的准确性和可靠性.
- 将拟议模型的性能与已建立的优化算法进行比较.
主要方法:
- 开发一个循环神经网络 (RNN) 模型.
- 集成动态适应性Al-Biruni地球半径 (DFBER) 算法用于参数优化.
- 使用RRMSE,NSE,MAE,MBE,r,R2和WI等指标进行比较分析.
- 使用ANOVA和Wilcoxon签名等级测试进行统计验证.
主要成果:
- 拟议的RNN-DFBER模型在BER,JAYA,FHO,WOA,GWO和PSO模型中表现出优越的性能.
- 该模型在预测风力发电数据模式方面实现了更高的准确性和更好的一致性.
- 统计分析证实了RNN-DFBER模型结果的意义和可靠性.
结论:
- RNN-DFBER模型为风力发电预测提供了更有效的方法.
- 这种先进的模型可以提高风力发电系统的可预测性和性能.
- 这些发现支持使用集成的人工智能和优化算法用于可再生能源预测.
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