摘要
针对农业温室大棚温度测量受噪声影响不易直接预测的问题,提出一种将XGBoost(extreme gradient boosting)和Kalman filter相结合的集成预测模型XGB-KF(extreme gradient boosting with Kalman filter)。该模型首先基于XGBoost对温室内部当前时刻的温度值进行初步估计,使用卡尔曼滤波(Kalman filter)对得到的估计结果进行动态修正,得到最终的预测结果。基于涿州地区农业温室大棚的传感器数据进行了数值实验,以均方根误差(root mean square error,RMSE)作为主要指标对模型进行性能评估。与XGBoost、Bi-LSTM和Bi-LSTM-KF方法相比较,XGB-KF的RMSE分别降低5.22%、10.85%、7.45%。
To address the challenge of agricultural greenhouse temperature measurement being highly susceptible to noise,which limits direct prediction accuracy,this study proposes an integrated prediction model,XGB-KF,combining XGBoost and the Kalman filter.First,the model estimates the current greenhouse temperature using XGBoost.Then,the Kalman filter dynamically adjusts the estimated result to refine the prediction.Numerical experiments are conducted using sensor data from a greenhouse in Zhuozhou,with root mean square error(RMSE)as the main evaluation metric.Compared with XGBoost,Bi-LSTM,and Bi-LSTM-KF methods,the XGB-KF model reduces RMSE by 5.22%,10.85%and 7.45%respectively.
作者
黄威
贾若然
钟坤华
刘曙光
HUANG Wei;JIA Ruoran;ZHONG Kunhua;LIU Shuguang(Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714,P.R.China;University of Chinese Academy of Sciences,Beijing 100049,P.R.China;Iflytek Co.,Ltd.,Hefei 230031,P.R.China)
出处
《重庆大学学报》
北大核心
2025年第4期108-114,共7页
Journal of Chongqing University
基金
中国科学院重点资助项目(E351600201)。
关键词
集成模型
机器学习
时间序列
温室温度
integrated model
machine learning
time series
greenhouse temperature
