To achieve effective intraday dispatch of photovoltaic(PV)power generation systems,a reliable ultra-shortterm power generation forecasting model is required.Based on a gradient boosting strategy and a dendritic networ...To achieve effective intraday dispatch of photovoltaic(PV)power generation systems,a reliable ultra-shortterm power generation forecasting model is required.Based on a gradient boosting strategy and a dendritic network,this paper proposes a novel ensemble prediction model,named gradient boosting dendritic network(GBDD)model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation.Unlike other machine learning models,the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation.In addition,based on the structure of GBDD,this paper proposes a strategy that can improve the prediction performance of other types of prediction models.The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models.The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.展开更多
Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are we...Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.展开更多
Rechargeable lithium-metal batteries that are operated based on reversible metal plating and stripping during the charge/discharge process are known for their high energy density far beyond the conventional,graphite-a...Rechargeable lithium-metal batteries that are operated based on reversible metal plating and stripping during the charge/discharge process are known for their high energy density far beyond the conventional,graphite-anode-based Li-ion batteries[1].However,the hostless structural evolution of Li metal during the anode process easily forms dendrites and could lead to a hazardous short circuit of batteries[2].In addition.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61973322 and 62103443)the Natural Science Foundation of Hunan Province,China(Grant No.2022JJ40630).
文摘To achieve effective intraday dispatch of photovoltaic(PV)power generation systems,a reliable ultra-shortterm power generation forecasting model is required.Based on a gradient boosting strategy and a dendritic network,this paper proposes a novel ensemble prediction model,named gradient boosting dendritic network(GBDD)model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation.Unlike other machine learning models,the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation.In addition,based on the structure of GBDD,this paper proposes a strategy that can improve the prediction performance of other types of prediction models.The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models.The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.
基金This work was partially supported by the National Natural Science Foundation of China(62073173,61833011)the Natural Science Foundation of Jiangsu Province,China(BK20191376)the Nanjing University of Posts and Telecommunications(NY220193,NY220145)。
文摘Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.
基金supported by the National Natural Science Foundation of China(22279028,22179018)the Natural Science Foundation of Hebei Province(B2021205019)the 333 Project of Hebei Province(C20231106).
文摘Rechargeable lithium-metal batteries that are operated based on reversible metal plating and stripping during the charge/discharge process are known for their high energy density far beyond the conventional,graphite-anode-based Li-ion batteries[1].However,the hostless structural evolution of Li metal during the anode process easily forms dendrites and could lead to a hazardous short circuit of batteries[2].In addition.
