一个基于遗传算法的组合框架,用于预测风速
Tathiana Mikamura Barchi1, João Lucas Ferreira Dos Santos1, Thiago Antonini Alves2
1Graduate Program in Industrial Engineering, Federal University of Technology - Paraná, 84017-220, Ponta Grossa, Brazil.
Scientific reports
|January 31, 2026
概括
准确的风速预测对于可再生能源至关重要. 一个基于新型遗传算法 (GA) 的组合框架显著改善了风速预测,提高了风能集成的可靠性.
科学领域:
- 可再生能源系统可再生能源系统
- 气象预报 气象预报
- 计算智能是一种计算智能.
背景情况:
- 风能是一个重要的清洁资源,但它的变化性需要准确的预测.
- 现有的风速预测模型与气象影响作斗争.
- 可靠的预测是管理风能间歇性的关键.
研究的目的:
- 开发和评估基于遗传算法 (GA) 的整体框架,用于增强风速预测.
- 系统地比较14种不同的预测模型的性能.
- 评估该框架在多个巴西城市的有效性.
主要方法:
- 提出了基于GA的整体方法,结合了各种预测模型.
- 评估了14种模型,包括线性,神经网络,混合和整体类型.
- 来自巴西五个城市的每分钟风速数据被用于模型验证.
主要成果:
- 基于GA的整体框架表现出高性能,平均平方误差 (MSE) 和平均绝对误差 (MAE) 值较低.
- 高R2值 (0.71390.8723) 表示强大的预测能力.
- 统计验证 (弗里德曼测试,p < 0.001) 证实了显著的模型性能差异和等级稳定性.
结论:
- 拟议的基于GA的整体框架在风速预测准确性方面取得了重大进展.
- 该框架具有很高的可扩展性和计算效率,使其适合实际应用.
- 改进的风速预测可以提高风能系统的整合性和可靠性.
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