Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy ...Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy problems.To address this research objective,this paper proposes a prediction model based on kernel principal component analysis(KPCA),modified cuckoo search algorithm(MCS)and deep convolutional neural networks(DCNN).Firstly,KPCA is utilized to reduce the dimension of the feature,which aims to reduce the redundant input vectors.Then using MCS to optimize the parameters of DCNN.Finally,the photovoltaic power forecasting method of KPCA-MCS-DCNN is established.In order to verify the prediction performance of the proposed model,this paper selects a photovoltaic power station in China for example analysis.The results show that the new hybrid KPCA-MCS-DCNN model has higher prediction accuracy and better robustness.展开更多
Traditional linear motor optimization methods typically use analytical models combined with intelligent optimization algorithms.However,this approach has disadvantages,e.g.,the analytical model might not be accurate e...Traditional linear motor optimization methods typically use analytical models combined with intelligent optimization algorithms.However,this approach has disadvantages,e.g.,the analytical model might not be accurate enough,and the intelligent optimization algorithm can easily fall into local optimization.A new linear motor optimization strategy combining an R-deep neural network(R-DNN)and modified cuckoo search(MCS)is proposed;additionally,the thrust lifting and thrust fluctuation reductions are regarded as optimization objectives.The R-DNN is a deep neural network modeling method using the rectified linear unit(RELU)activation function,and the MCS provides a faster convergence speed and stronger data search capability as compared with genetic algorithms,particle swarm optimization,and standard CS algorithms.Finally,the validity and accuracy of this work are proven based on prototype experiments.展开更多
文摘Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy problems.To address this research objective,this paper proposes a prediction model based on kernel principal component analysis(KPCA),modified cuckoo search algorithm(MCS)and deep convolutional neural networks(DCNN).Firstly,KPCA is utilized to reduce the dimension of the feature,which aims to reduce the redundant input vectors.Then using MCS to optimize the parameters of DCNN.Finally,the photovoltaic power forecasting method of KPCA-MCS-DCNN is established.In order to verify the prediction performance of the proposed model,this paper selects a photovoltaic power station in China for example analysis.The results show that the new hybrid KPCA-MCS-DCNN model has higher prediction accuracy and better robustness.
基金Supported by the National Natural Science Foundation of China(51837001,51907001,51707002).
文摘Traditional linear motor optimization methods typically use analytical models combined with intelligent optimization algorithms.However,this approach has disadvantages,e.g.,the analytical model might not be accurate enough,and the intelligent optimization algorithm can easily fall into local optimization.A new linear motor optimization strategy combining an R-deep neural network(R-DNN)and modified cuckoo search(MCS)is proposed;additionally,the thrust lifting and thrust fluctuation reductions are regarded as optimization objectives.The R-DNN is a deep neural network modeling method using the rectified linear unit(RELU)activation function,and the MCS provides a faster convergence speed and stronger data search capability as compared with genetic algorithms,particle swarm optimization,and standard CS algorithms.Finally,the validity and accuracy of this work are proven based on prototype experiments.
