使用条件局部卷积和多维气象特征进行时空空间风速预测
Meng Wang1,2, Juanle Wang3,4,5, Mingming Yu6
1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences, Beijing, 100101, China.
Scientific reports
|November 1, 2024
概括
这项研究通过使用新的深度学习模型,增强了高地风速预测. 改进的条件局部卷积循环网络 (CLCRN) 提供了更准确的风力发电预测和更好的洞察力.
科学领域:
- 气象学 天气学
- 可再生能源系统可再生能源系统
- 人工智能的人工智能
背景情况:
- 精确的风速预测对于高效的风力发电预测和运营成本降低至关重要,特别是在复杂的高地地形.
- 传统的预测方法和现有的深度学习模型由于统一的影响权重假设而与局部气象变化作斗争.
研究的目的:
- 引入一个增强的条件局部卷积循环网络 (CLCRN),以改善时空空间风速预测.
- 解决风速预测深度学习模型中统一影响重量结构的局限性.
主要方法:
- 开发了一个增强的CLCRN模型,包含多维气象输入 (温度,压力,露点,风成分).
- 重新设计的卷积内核捕捉了当地的气象特征并整合了多种影响因素,克服了统一的重量问题.
- 使用2019年至2021年的气象站数据验证了该模型,用于3,6,9和12小时的预测间隔.
主要成果:
- 与所有预测间隔的其他模型相比,增强的CLCRN模型始终实现了较低的平均绝对误差 (MAE) 和根平均平方误差 (RMSE).
- 当地卷积权重的空间分布显示与当地的风速模式保持一致,提高了模型的解释性.
- 该模型的性能使用真实世界气象数据进行了验证.
结论:
- 增强的CLCRN模型显著提高了在具有挑战性的高地地区的时空风速预测精度.
- 该模型捕捉当地特征的能力及其增强的解释性为可再生能源规划和风力动力学模拟提供了实际的好处.
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