Scholarlink

<p>Greenhouses are a critical component of modern agriculture, facilitating crop growth and development, and accurate predictions of temperature and humidity are essential for mitigating crop diseases and optimizing the growth environment. However, short- and medium-term forecasts of temperature and humidity are challenging because of the complexity of greenhouse microclimates. This paper presents a hybrid model that integrates a frequency-enhanced channel attention mechanism optimized with a temporal convolutional network (TCN-FECAM) and an iTransformer. The model employs a cross-attention mechanism incorporating the advantages of the two models, and a 48-sequence sliding window strategy is used to ensure accurate multistep predictions of temperature and humidity over spans of 3&#xa0;h to 24&#xa0;h. The experimental results demonstrate that the TCN-FECAM-iTransformer model outperforms other models across diverse time scales, including GRU, LSTM, Informer, Autoformer, Crossformer, FAM-LSTM, and TPA-LSTM. Specifically, in temperature prediction, the model achieves R<sup>2</sup> coefficients of 0.979, 0.973, 0.968, and 0.953 and RMSE values of 0.657, 0.806, 0.923, and 1.126, for 3&#xa0;h, 6&#xa0;h, 12&#xa0;h, and 24&#xa0;h intervals, respectively. In humidity prediction, the model obtains R<sup>2</sup> coefficients of 0.976, 0.961, 0.947, and 0.939 and RMSE values of 1.805, 2.567, 3.132, and 3.451 for 3&#xa0;h, 6&#xa0;h, 12&#xa0;h, and 24&#xa0;h intervals, respectively. The model therefore exhibits reliable performance in predicting temperature and humidity in greenhouse environments, offering robust support for monitoring and early warnings in crop growth environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-step prediction method of temperature and humidity based on TCN-FECAM-iTransformer

  • 期刊论文

  • 发表日期：

    2025年4月24日

查看全文
  • Zongxu Xie,
  • Zhiqing Tao,
  • Xianhong Xie,
  • Yuan Rao,
  • Ke Li,
  • Wei Li,
  • Jun Zhu

摘要

Greenhouses are a critical component of modern agriculture, facilitating crop growth and development, and accurate predictions of temperature and humidity are essential for mitigating crop diseases and optimizing the growth environment. However, short- and medium-term forecasts of temperature and humidity are challenging because of the complexity of greenhouse microclimates. This paper presents a hybrid model that integrates a frequency-enhanced channel attention mechanism optimized with a temporal convolutional network (TCN-FECAM) and an iTransformer. The model employs a cross-attention mechanism incorporating the advantages of the two models, and a 48-sequence sliding window strategy is used to ensure accurate multistep predictions of temperature and humidity over spans of 3 h to 24 h. The experimental results demonstrate that the TCN-FECAM-iTransformer model outperforms other models across diverse time scales, including GRU, LSTM, Informer, Autoformer, Crossformer, FAM-LSTM, and TPA-LSTM. Specifically, in temperature prediction, the model achieves R2 coefficients of 0.979, 0.973, 0.968, and 0.953 and RMSE values of 0.657, 0.806, 0.923, and 1.126, for 3 h, 6 h, 12 h, and 24 h intervals, respectively. In humidity prediction, the model obtains R2 coefficients of 0.976, 0.961, 0.947, and 0.939 and RMSE values of 1.805, 2.567, 3.132, and 3.451 for 3 h, 6 h, 12 h, and 24 h intervals, respectively. The model therefore exhibits reliable performance in predicting temperature and humidity in greenhouse environments, offering robust support for monitoring and early warnings in crop growth environments.