通过修改复杂地形的表面粗度参数化来增强基于ANN的风力发电预测
Jeongwon Kim1, Ho-Jeong Shin1, Keunmin Lee2
1Ecosystem-Atmosphere Process Laboratory, Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea.
Journal of environmental management
|June 1, 2024
概括
改善复杂地形的风能预测对于可再生能源至关重要. 这项研究通过改进大气模型和使用机器学习来增强风能预测,提高了14%的准确性.
科学领域:
- 大气科学 大气科学
- 可再生能源系统可再生能源系统
- 计算气象学 计算气象学
背景情况:
- 精确的风能资源评估和预测对于将风能纳入电网至关重要,特别是对于位于复杂地形的陆上风电场.
- 预测的挑战源于行星边界层内的表面异质性和乱的大气流之间的复杂相互作用.
- 现有的数值天气预测 (NWP) 模型通常需要增强的表面粗度参数化,以准确地捕捉不同景观上的风力动态.
研究的目的:
- 加强对风能资源的评估,并改善复杂的丘陵地形的中期风能预测.
- 研究将现实的表面粗度效应纳入NWP模型以更好地预测风速的影响.
- 评估将NWP模型输出与机器学习技术用于前一天风力发电预测的有效性.
主要方法:
- 利用了数字天气预报 (NWP) 模型,对子网尺度地形,粗度子层和天花板高度进行了高级参数化.
- 集成机器学习,特别是人工神经网络 (ANN),以处理NWP模型输出.
- 与陆上风电场的24台风力轮机观察到的枢纽高度风速数据对比,验证了模型性能.
主要成果:
- NWP模型成功地复制了观察到的风速分布,持续时间和时空变化,当包括增强的表面粗度效应时.
- 该研究确定了用于机器学习应用的大气模型中的有益特征,代表了表面粗性的异质性.
- 将NWP模型输出与ANN相结合,在年度正常化平均绝对误差中,前一天的风能预测得到了14%的改进.
结论:
- 在大气模型中提高表面摩擦的参数化对于准确的风力发电预测和资源评估至关重要.
- NWP模型与机器学习技术的整合为陆上风电预测提供了重大进步,特别是在复杂的山区.
- 这项研究为优化风能评估和预测在具有挑战性的地理区域提供了宝贵的见解.
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