基于模糊颗粒的风速预测与多目标优化
Chi Zhang1, Jianzhou Wang2, Zhiwu Li1
1Institute of Systems Engineering, Macau University of Science and Technology, Macau, 999078, China.
Scientific reports
|December 29, 2025
概括
准确的风力发电预测对于可再生能源的整合至关重要. 一个新的模糊信息粒度和多目标优化框架提高了风速预测的准确性和效率.
科学领域:
- 能源系统 能源系统
- 人工智能的人工智能
- 可再生能源可再生能源是可再生能源.
背景情况:
- 准确的风力发电预测对于整合可再生能源和支持脱碳至关重要.
- 风力资源的随机性质对短期到中期预测提出了挑战,导致运营不确定性.
- 现有的预测模型往往难以保持一致的高准确性,这推动了对方法进步的需求.
研究的目的:
- 引入一个新的多尺度预测框架,以改善风力发电预测.
- 通过提高准确性和可靠性来解决当前预测技术的局限性.
- 为了有效地管理预测准确性和计算效率之间的权衡.
主要方法:
- 整合模糊信息粒度以捕捉内在特征并减少来自挥发性风速数据的数据复杂性.
- 使用多目标优化策略,使用多个神经网络,采用不同的预测原则.
- 通过启发式优化算法进行神经网络输出的自适应集成,以提高预测.
主要成果:
- 拟议的框架证明了来风电场数据集的卓越表现.
- 模糊信息粒度有效地减轻了深度学习模型的噪音干扰.
- 协作优化算法成功平衡了预测准确性和计算效率.
结论:
- 新的多尺度预测框架显著提高了风力发电预测的准确性和稳定性.
- 模糊信息颗粒是有效处理复杂和挥发性的风速数据.
- 开发的方法为提高风能系统的可靠性提供了可行的解决方案.
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