研究基于长期短期记忆和注意力机制的农电气系统中微气候的时间序列预测
Long Zhang1,2, Jianhui Gong2, Cuinan Wu2,3
1College of Engineering, Nanjing Agricultural University, Nanjing, China.
Frontiers in plant science
|March 9, 2026
概括
一个新的LSTM-Attention模型准确地预测了农业电压 (AV) 系统中的微气候条件. 这种先进的模型改善了对太阳辐射和空气温度的预测,这对于优化这些双重用途农田的作物管理至关重要.
科学领域:
- 农业科学 农业科学
- 可再生能源系统可再生能源系统
- 环境建模环境建模
背景情况:
- 农电 (AV) 系统将光伏 (PV) 发电与农业相结合,由于时间和空间的变化,在准确建模其内部微气候方面存在挑战.
- 现有的模型往往无法捕捉到AV系统中的复杂动态,阻碍了最佳的土地利用和能源生产战略.
研究的目的:
- 开发和评估一个先进的深度学习模型,用于预测AV系统内的微气候参数.
- 解决传统模型在捕捉太阳辐射强度和空气温度在AV环境中的时间和空间变化的局限性.
主要方法:
- 利用长短期记忆 (LSTM) 网络与集成的注意力机制来处理时间序列环境数据.
- 纳入太阳辐射强度和空气温度作为关键输入参数,使用来自中国南京不同光伏面板密度的实验AV系统的数据.
- 将LSTM-Attention模型的预测性能与传统机器学习方法和标准LSTM模型进行了比较.
主要成果:
- 在预测太阳辐射强度和空气温度方面,LSTM-Attention模型显著超过了基线模型.
- 实现了根平均平方误差 (RMSE) 的实质性降低:辐射强度为28.0%42.1%和空气温度为39.0% (夏季) /18.1% (冬季).
- 在不同的光伏面板密度和具有挑战性的天气条件 (包括冬季和雨季) 中表现出强大而稳定的预测性能.
结论:
- LSTM-Attention模型有效地捕捉了AV系统内的微气候参数的复杂时间变化.
- 这些发现为加强作物管理策略和优化AV系统的设计和运行提供了至关重要的理论支持.
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