您好，欢迎访问江苏省农业科学院 机构知识库！
江苏省农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

A variable weight combination prediction model for climate in a greenhouse based on BiGRU-Attention and LightGBM

文献类型： 外文期刊

作者： Mao, Xiaojuan 1 ; Ren, Ni 1 ; Dai, Peiyu 1 ; Jin, Jing 1 ; Wang, Baojia 1 ; Kang, Rui 1 ; Li, Decui 1 ;

作者机构： 1.Jiangsu Acad Agr Sci, Agr Informat Inst, Nanjing 210014, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Intelligent Agr Technol Changjiang Delta, Nanjing 210014, Peoples R China

关键词： Greenhouse; Climate; Bi-directional Gated Recurrent Unit; Attention; LightGBM

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 219 卷

页码：

收录情况： SCI

摘要： Accurate prediction of environmental changes in a greenhouse is crucial for precise control and promoting crop growth. However, the microclimate environment in a greenhouse is nonlinear, temporal, multivariate, and strongly coupled, making it difficult to establish a robust fitting model. To address these issues, this study proposed a variable weight combination prediction model based on attention mechanism optimized Bidirectional Gated Recurrent Unit (BiGRU-Attention) and Light Gradient Boosting Machine (LightGBM). The Particle Swarm Optimization Algorithm (PSO) was employed to optimize the weight coefficients of the predicted values from BiGRU-Attention and LightGBM models at different times. This optimization aimed to enhance the accuracy of predicting air temperature, air humidity, and Photosynthetically Active Radiation (PAR) in a greenhouse. In predictions spanning time steps from 30 to 120 min, the variable weight combination prediction model demonstrated superior performance compared to single models and equal weight combination model BiGRU-Attention-LightGBM. At the time step of 120 min, the coefficient of determination R2 for air temperature was 0.9586, air humidity was 0.9232, and PAR was 0.8066. This indicated that the variable weight combination model (PSO-BiGRU-Attention-LightGBM) could more accurately predict the future dynamic trends of climatic environment factors in a greenhouse.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：江苏省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台