1.中国科学院重庆绿色智能技术研究院，重庆 400714;2.中国科学院大学，北京 100049;3.科大讯飞股份有限公司，合肥 230031
黄威（1998—），男，硕士研究生，主要从事数据挖掘、时间序列预测方向研究。
刘曙光，男，硕士生导师，(E-mail)liushuguang@cigit.ac.cn。
TP399
中国科学院重点资助项目（E351600201）。
1.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, P. R. China;2.University of Chinese Academy of Sciences, Beijing 100049, P. R. China;3.Iflytek Co., Ltd., Hefei 230031, P. R. China
Surpported by Key Research Programs of Chinese Academy of Sciences (E351600201).
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.
黄威,贾若然,钟坤华,刘曙光.基于
