SOI-SIMNI (Silicon On Insulator-Separation by Implanted Nitrogen) films were formed with standard method and multiple-step implantation one. The Hall-e ffect measurements (4—300K) show that the multiple-step implante...SOI-SIMNI (Silicon On Insulator-Separation by Implanted Nitrogen) films were formed with standard method and multiple-step implantation one. The Hall-e ffect measurements (4—300K) show that the multiple-step implanted SIMNI films have a lower sheet resistance and higher carrier mobility than those in standar d SIMNI films. The DLTS results indicate that there is a deep level defect (E_t=0.152 eV) in the standard SIMNI films but no such defects in the multip le-step implanted ones. The multiple-step implanted SIMNI films have good elec trical properties.展开更多
The strong stochastic disturbance caused by largescale distributed energy access to power grids affects the security,stability and economic operations of the power grid.A novel multiple-step greedy policy based on the...The strong stochastic disturbance caused by largescale distributed energy access to power grids affects the security,stability and economic operations of the power grid.A novel multiple-step greedy policy based on the consensus Qlearning(MSGP-CQ)strategy is proposed in this paper,which is an automatic generation control(AGC)for distributed energy incorporating multiple-step greedy attribute and multiple-level allocation strategy.The convergence speed and learning efficiency in the MSGP algorithm are accelerated through the predictive multiple-step iteration updating in the proposed strategy,and the CQ algorithm is adopted with collaborative consensus and selflearning characteristics to enhance the adaptability of the power allocation strategy under the strong stochastic disturbances and obtain the total power commands in the power grid and the dynamic optimal allocations of the unit power.The simulations of the improved IEEE two-area load-frequency control(LFC)power system and the interconnected system model of intelligent distribution network(IDN)groups incorporating a large amount of distributed energy show that the proposed strategy can achieve the optimal coordinated control and power allocation in the power grid.The algorithm MSGP-CQ has stronger robustness and faster dynamic optimization speed and can reduce generation costs.Meanwhile it can also solve the strong stochastic disturbance caused by large-scale distributed energy access to the grid compared with some existing intelligent algorithms.展开更多
Accurate short-term forecasting of heating energy demand is needed for achieving optimal building energy management,cost savings,environmental sustainability,and responsible energy consumption.Furthermore,short-term h...Accurate short-term forecasting of heating energy demand is needed for achieving optimal building energy management,cost savings,environmental sustainability,and responsible energy consumption.Furthermore,short-term heating energy prediction contributes to zero-energy building performance in cold climates.Given the critical importance of short-term forecasting in heating energy management,this study evaluated six prevalent deep-learning algorithms to predict energy load,including single and hybrid models.The overall best-performing predictors were hybrid models using Convolutional Neural Networks,regardless of whether they were multivariate or univariate.Nevertheless,while the multivariate models performed better in the first hour,the univariate models often were more accurate in the final 24 h.Thus,the best-performing predictor of the first timestep was a multivariate hybrid Convolutional Neural Network–Recurrent Neural Network model with a coefficient of determination(R^(2))of 0.98 and the lowest mean absolute error.Yet,the best-performing predictor of the final timestep was the univariate hybrid model Convolutional Neural Network–Long Short-Term Memory with an R^(2)of 0.80.Also,the prediction accuracy of the best-performing multivariate hybrid models reduced faster per hour compared to the univariate models.These findings suggest that multivariate models may be better suited for early timestep predictions,while univariate models may be better suited for later time steps.Hence,combining the models can enhance accuracy at various timesteps for achieving high fidelity in forecasting and offering a comprehensive tool for energy management.展开更多
基金Project Supported by National Natural Science Foundation of China Under Grant !No.697760 0 6.
文摘SOI-SIMNI (Silicon On Insulator-Separation by Implanted Nitrogen) films were formed with standard method and multiple-step implantation one. The Hall-e ffect measurements (4—300K) show that the multiple-step implanted SIMNI films have a lower sheet resistance and higher carrier mobility than those in standar d SIMNI films. The DLTS results indicate that there is a deep level defect (E_t=0.152 eV) in the standard SIMNI films but no such defects in the multip le-step implanted ones. The multiple-step implanted SIMNI films have good elec trical properties.
基金This work was supported in part by the National Natural Science Foundation of China(No.51707102).
文摘The strong stochastic disturbance caused by largescale distributed energy access to power grids affects the security,stability and economic operations of the power grid.A novel multiple-step greedy policy based on the consensus Qlearning(MSGP-CQ)strategy is proposed in this paper,which is an automatic generation control(AGC)for distributed energy incorporating multiple-step greedy attribute and multiple-level allocation strategy.The convergence speed and learning efficiency in the MSGP algorithm are accelerated through the predictive multiple-step iteration updating in the proposed strategy,and the CQ algorithm is adopted with collaborative consensus and selflearning characteristics to enhance the adaptability of the power allocation strategy under the strong stochastic disturbances and obtain the total power commands in the power grid and the dynamic optimal allocations of the unit power.The simulations of the improved IEEE two-area load-frequency control(LFC)power system and the interconnected system model of intelligent distribution network(IDN)groups incorporating a large amount of distributed energy show that the proposed strategy can achieve the optimal coordinated control and power allocation in the power grid.The algorithm MSGP-CQ has stronger robustness and faster dynamic optimization speed and can reduce generation costs.Meanwhile it can also solve the strong stochastic disturbance caused by large-scale distributed energy access to the grid compared with some existing intelligent algorithms.
基金funded by the Natural Sciences and Engineering Research Council(NSERC)Discovery Grant,grant number RGPIN-05481.
文摘Accurate short-term forecasting of heating energy demand is needed for achieving optimal building energy management,cost savings,environmental sustainability,and responsible energy consumption.Furthermore,short-term heating energy prediction contributes to zero-energy building performance in cold climates.Given the critical importance of short-term forecasting in heating energy management,this study evaluated six prevalent deep-learning algorithms to predict energy load,including single and hybrid models.The overall best-performing predictors were hybrid models using Convolutional Neural Networks,regardless of whether they were multivariate or univariate.Nevertheless,while the multivariate models performed better in the first hour,the univariate models often were more accurate in the final 24 h.Thus,the best-performing predictor of the first timestep was a multivariate hybrid Convolutional Neural Network–Recurrent Neural Network model with a coefficient of determination(R^(2))of 0.98 and the lowest mean absolute error.Yet,the best-performing predictor of the final timestep was the univariate hybrid model Convolutional Neural Network–Long Short-Term Memory with an R^(2)of 0.80.Also,the prediction accuracy of the best-performing multivariate hybrid models reduced faster per hour compared to the univariate models.These findings suggest that multivariate models may be better suited for early timestep predictions,while univariate models may be better suited for later time steps.Hence,combining the models can enhance accuracy at various timesteps for achieving high fidelity in forecasting and offering a comprehensive tool for energy management.
