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A dual deep learning approach for winter temperature prediction in solar greenhouses in Northern China

文献类型： 外文期刊

作者： Yu, Jingxin 1 ; Zhao, Jinpeng 3 ; Sun, Congcong 2 ; Zhang, Ruochen 1 ; Zheng, Wengang 1 ; Xu, Linlin 5 ; Wei, Xiaoming 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment Technol, Beijing 100097, Peoples R China

2.Wageningen Univ, Agr Biosyst Engn Grp, NL-6700 AA Wageningen, Netherlands

3.Natl Engn Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

4.Xian Technol Univ, Coll Mech & Elect Engn, Xian 710021, Peoples R China

5.Univ Calgary, Dept Geometr Engn, 2500 Univ Dr NW, Calgary, AB T2N 1N4, Canada

关键词： Winter temperature prediction; Solar greenhouse; Northern china; Dual deep learning; Optimal cultivation

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 229 卷

页码：

收录情况： SCI

摘要： Achieving accurate and efficient winter temperature prediction in solar greenhouses is critically important but challenging for greenhouse cultivation, especially in northern China. To address this challenge, this study proposes three key innovations: (1) a dual deep learning architecture integrating Transformer's global modeling with BiLSTM's local processing; (2) enhanced feature extraction through relative positional encoding and hybrid sparse attention; and (3) optimized computational efficiency via time embedding strategies. The proposed model was compared with 11 typical deep learning models (including ResNet, GRU, and TCN) through systematic experiments involving multiple input feature combinations (5-18 features) and various prediction durations (6-48 h). The results indicate that the proposed model outperforms typical deep learning models across all evaluation metrics, including an average MSE of 0.0599 degrees C and an R2 of 0.9989 in lightweight configurations, a high improvement over the best existing methods. Furthermore, the proposed model demonstrates exceptional stability (MSE standard deviation < 0.01 degrees C) and generalization ability (R2 > 0.98) under different input combinations and prediction durations. This study identifies indoor temperature, vapor pressure deficit (VPD), and indoor solar radiation as the three most critical factors influencing the prediction accuracy. Through comprehensive feature combinations, the prediction performance improves significantly with MSE reduced by 70.1% (from 0.1959 degrees C to 0.0586 degrees C). Among all scenarios, the 288-144 configuration (24- hour input predicting 12 h) achieves optimal balance of accuracy and efficiency. Even in challenging long-term scenarios like 576-576 (48-hour input predicting 48 h), the model still maintains good accuracy (R2=0.9665). This research provides new technological support with an accurate and efficient winter temperature prediction model for the intelligent control of greenhouse environments, offering important implications for optimal greenhouse cultivation.

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