Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.B...In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.展开更多
Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent var...Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.展开更多
The state prediction based on the unscented Kalman filter （UKF） for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of a...The state prediction based on the unscented Kalman filter （UKF） for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval （Ci） is proposed to overcome the problem. The advantages of CI are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results.展开更多
作为电力系统中的基本量测设备,电子式电压互感器(electronic voltage transformers,EVTs)的测量精度对系统的监控、控制与安全运行至关重要。为此,提出了一种基于混合深度模型和自适应窗宽概率密度估计的互感器测量误差区间预测模型。...作为电力系统中的基本量测设备,电子式电压互感器(electronic voltage transformers,EVTs)的测量精度对系统的监控、控制与安全运行至关重要。为此,提出了一种基于混合深度模型和自适应窗宽概率密度估计的互感器测量误差区间预测模型。首先,通过改进的集合经验模态分解对历史比差特征进行数据前处理。其次,提出了基于数据驱动的双向时序卷积网络、双向门控循环单元和多头注意力机制混合深度学习模型,对分解后的不同模态分量进行预测。此外,引入自适应选择最优窗宽的核密度概率估计方法,拟合预测结果构建不同置信度下的预测区间,并比较不同核函数对于预测区间的影响。通过算例分析,验证了所提模型在提高确定性预测和概率区间预测准确度方面的有效性。展开更多
隐蔽火区地表碳通量监测对评估其温室效应及火区范围圈定十分重要。针对碳通量影响因素多、难预测等问题,提出了一种基于灰狼优化算法(Grey Wolf Optimization,GWO)-改进高斯过程回归(Gaussian Process Regression,GPR)-核密度估计(Kern...隐蔽火区地表碳通量监测对评估其温室效应及火区范围圈定十分重要。针对碳通量影响因素多、难预测等问题,提出了一种基于灰狼优化算法(Grey Wolf Optimization,GWO)-改进高斯过程回归(Gaussian Process Regression,GPR)-核密度估计(Kernel Density Estimation,KDE)预测模型。采用格拉布斯检验法剔除异常值,运用GWO优化GPR算法中的超参数,以提高预测精度。使用验证集预测误差并进行KDE建模,得到碳通量的区间预测值,进而针对组合模型的泛化能力及参数敏感性分析进行评估。结果显示:GWO-GPR-KDE模型的平均绝对误差、均方根误差、决定系数、80%置信区间宽度和95%置信区间宽度分别为0.95386、1.2663、0.92656、0.387和0.823,这些评估指标均优于随机森林(Random Forest,RF)、反向传播神经网络(Back Propagation Neural Network,BPNN)、结合多层感知器的支持向量机(Multilayer Perceptron-Support Vector Machine,MLP-SVM)、高斯过程回归(Gaussian Process Regression,GPR)等经典模型。GWO-GPR-KDE模型对隐蔽火区地表碳通量预测具有较好的准确性和泛化性,为煤田火区防控和温室效应评估提供了新思路。展开更多
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
基金supported by the National Natural Science Foundation of China(7084001290924022)the Ph.D.Thesis Innovation and Excellent Foundation of Nanjing University of Aeronautics and Astronautics(2010)
文摘In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.
文摘Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.
基金supported by the National Natural Science Foundation of China(60574088608740536103406)
文摘The state prediction based on the unscented Kalman filter （UKF） for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval （Ci） is proposed to overcome the problem. The advantages of CI are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results.
文摘作为电力系统中的基本量测设备,电子式电压互感器(electronic voltage transformers,EVTs)的测量精度对系统的监控、控制与安全运行至关重要。为此,提出了一种基于混合深度模型和自适应窗宽概率密度估计的互感器测量误差区间预测模型。首先,通过改进的集合经验模态分解对历史比差特征进行数据前处理。其次,提出了基于数据驱动的双向时序卷积网络、双向门控循环单元和多头注意力机制混合深度学习模型,对分解后的不同模态分量进行预测。此外,引入自适应选择最优窗宽的核密度概率估计方法,拟合预测结果构建不同置信度下的预测区间,并比较不同核函数对于预测区间的影响。通过算例分析,验证了所提模型在提高确定性预测和概率区间预测准确度方面的有效性。
文摘隐蔽火区地表碳通量监测对评估其温室效应及火区范围圈定十分重要。针对碳通量影响因素多、难预测等问题,提出了一种基于灰狼优化算法(Grey Wolf Optimization,GWO)-改进高斯过程回归(Gaussian Process Regression,GPR)-核密度估计(Kernel Density Estimation,KDE)预测模型。采用格拉布斯检验法剔除异常值,运用GWO优化GPR算法中的超参数,以提高预测精度。使用验证集预测误差并进行KDE建模,得到碳通量的区间预测值,进而针对组合模型的泛化能力及参数敏感性分析进行评估。结果显示:GWO-GPR-KDE模型的平均绝对误差、均方根误差、决定系数、80%置信区间宽度和95%置信区间宽度分别为0.95386、1.2663、0.92656、0.387和0.823,这些评估指标均优于随机森林(Random Forest,RF)、反向传播神经网络(Back Propagation Neural Network,BPNN)、结合多层感知器的支持向量机(Multilayer Perceptron-Support Vector Machine,MLP-SVM)、高斯过程回归(Gaussian Process Regression,GPR)等经典模型。GWO-GPR-KDE模型对隐蔽火区地表碳通量预测具有较好的准确性和泛化性,为煤田火区防控和温室效应评估提供了新思路。
