[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the cha...[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.展开更多
针对独立微电网内源-荷供需量不平衡问题,文章在孤岛状态下的微电网需求侧添加了功率型和能量型相结合的混合储能系统。对微电网内满足功率平衡约束的净负荷功率序列信号使用变分模态分解法(VMD)进行混合储能的优化配置,利用中心频率法...针对独立微电网内源-荷供需量不平衡问题,文章在孤岛状态下的微电网需求侧添加了功率型和能量型相结合的混合储能系统。对微电网内满足功率平衡约束的净负荷功率序列信号使用变分模态分解法(VMD)进行混合储能的优化配置,利用中心频率法结合皮尔逊相关系数(Pearson)确定最优的分解层数,对分解结果采用短时傅里叶变换(STFT)进行时频分析,得出各分量的模态混叠情况,再根据不同类型储能的充放电频率响应特性进行高、低频功率的重构和分配;对分配结果采用可靠容量计算方法配置储能系统的额定容量和功率,计算配置总成本,并以储能元件的荷电状态(State of Charge,SOC)为依据,衡量系统的供电可靠性。通过对比使用经验模态分解法(EMD)和传统一阶低通滤法的研究结果,VMD分解方法能够有效克服EMD的模态混叠现象,同时提高系统配置的经济性及供电可靠性。展开更多
文摘[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.
文摘针对独立微电网内源-荷供需量不平衡问题,文章在孤岛状态下的微电网需求侧添加了功率型和能量型相结合的混合储能系统。对微电网内满足功率平衡约束的净负荷功率序列信号使用变分模态分解法(VMD)进行混合储能的优化配置,利用中心频率法结合皮尔逊相关系数(Pearson)确定最优的分解层数,对分解结果采用短时傅里叶变换(STFT)进行时频分析,得出各分量的模态混叠情况,再根据不同类型储能的充放电频率响应特性进行高、低频功率的重构和分配;对分配结果采用可靠容量计算方法配置储能系统的额定容量和功率,计算配置总成本,并以储能元件的荷电状态(State of Charge,SOC)为依据,衡量系统的供电可靠性。通过对比使用经验模态分解法(EMD)和传统一阶低通滤法的研究结果,VMD分解方法能够有效克服EMD的模态混叠现象,同时提高系统配置的经济性及供电可靠性。
