For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the i...For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.展开更多
This paper shows how the so called von Karman model can be obtained as a singular limit of a modified Mindlin-Timoshenko system when the modulus of elasticity in shear κ tends to infinity, provided a regularizing ter...This paper shows how the so called von Karman model can be obtained as a singular limit of a modified Mindlin-Timoshenko system when the modulus of elasticity in shear κ tends to infinity, provided a regularizing term through a fourth order dispersive operator is added. Introducing damping mechanisms, the authors also show that the energy of solutions for this modified Mindlin-Timoshenko system decays exponentially, uniformly with respect to the parameter k. As κ→∞, the authors obtain the damped von Karman model with associated energy exponentially decaying to zero as well.展开更多
基金National Natural Science Foundation of China(No.51467008)。
文摘For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.
基金supported by INCTMat, FAPESQ-PB, CNPq (Brazil) under Grant Nos. 308150/2008-2 and 620108/2008-8the MICINN (Spain) under Grant No. MTM2008-03541+1 种基金the Advanced Grant FP7-246775 NUMERIWAVES of the ERCthe Project PI2010-04 of the Basque Government
文摘This paper shows how the so called von Karman model can be obtained as a singular limit of a modified Mindlin-Timoshenko system when the modulus of elasticity in shear κ tends to infinity, provided a regularizing term through a fourth order dispersive operator is added. Introducing damping mechanisms, the authors also show that the energy of solutions for this modified Mindlin-Timoshenko system decays exponentially, uniformly with respect to the parameter k. As κ→∞, the authors obtain the damped von Karman model with associated energy exponentially decaying to zero as well.
