Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces a...Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-kNN-P that applies kNN on each decomposed intrinsic mode function (IMF) and residue for separate modelling and forecasting followed by summation and an EMD-kNN-M that forms a feature vector set from all IMFs and residue followed by a single kNN modelling and forecasting. These two configurations are compared with the persistent model and the conventional kNN model on a wind speed time series dataset from Singapore. The results show that the two EMD-kNN hybrid models have good performance for longer term forecasting and EMD-kNN-M has better performance than EMD-kNN-P for shorter term forecasting.展开更多
We report a theoretical work on the properties of modulational instability and bright type nonlinear localized modes in one-dimensional easy-axis weak ferromagnetic spin lattices involving next-nearest-neighbor coupli...We report a theoretical work on the properties of modulational instability and bright type nonlinear localized modes in one-dimensional easy-axis weak ferromagnetic spin lattices involving next-nearest-neighbor couplings.With a linear stability analysis, we calculate the growth rates of the modulational instability, and plot the instability regions.When the strength of the next-nearest-neighbor coupling is large enough, two new asymmetric modulational instability regions appear near the boundary of the first Brillouin zone.Furthermore, analytical forms of the bright nonlinear localized modes are constructed by means of a quasi-discreteness approach.The influence of the next-nearest-neighbor coupling on the Brillouin zone center mode and boundary mode are discussed.In particular, we discover a reversal phenomenon of the propagation direction of the Brillouin zone boundary mode.展开更多
Mode tracking is required in the structural optimization when the frequencies of certain specified modes must be maintained within a suitable range.A simple tracking method employing the mode number is invalid or misl...Mode tracking is required in the structural optimization when the frequencies of certain specified modes must be maintained within a suitable range.A simple tracking method employing the mode number is invalid or misleading when local modes appear or disappear during mesh updating.In this work,a mode tracking scheme combining the nearest neighbor method(NNM)with the modal assurance criterion(MAC)is proposed.Several NNM algorithms are compared,and the k-dimensional tree(kd-tree)NNM is used to transform eigenvectors(mode shapes)from different scales to identical one.A threshold determination method is implemented for the MAC to assess the similarities in all the calculated modes.On the basis of the mode tracking scheme,specified modes can be tracked between different finite element method(FEM)models which have different meshes and optimized shapes.The effectiveness is verified through an example of shape optimization using an electric motor structure.展开更多
文摘Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-kNN-P that applies kNN on each decomposed intrinsic mode function (IMF) and residue for separate modelling and forecasting followed by summation and an EMD-kNN-M that forms a feature vector set from all IMFs and residue followed by a single kNN modelling and forecasting. These two configurations are compared with the persistent model and the conventional kNN model on a wind speed time series dataset from Singapore. The results show that the two EMD-kNN hybrid models have good performance for longer term forecasting and EMD-kNN-M has better performance than EMD-kNN-P for shorter term forecasting.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11604121 and 11875126)the Natural Science Fund Project of Hunan Province,China(Grant No.2017JJ3255)+1 种基金the National College Students’ Innovation Entrepreneurship Training Program,China(Grant No.201810531014)the Scientific Research Fund of Hunan Provincial Education Department,China(Grant No.17B212)
文摘We report a theoretical work on the properties of modulational instability and bright type nonlinear localized modes in one-dimensional easy-axis weak ferromagnetic spin lattices involving next-nearest-neighbor couplings.With a linear stability analysis, we calculate the growth rates of the modulational instability, and plot the instability regions.When the strength of the next-nearest-neighbor coupling is large enough, two new asymmetric modulational instability regions appear near the boundary of the first Brillouin zone.Furthermore, analytical forms of the bright nonlinear localized modes are constructed by means of a quasi-discreteness approach.The influence of the next-nearest-neighbor coupling on the Brillouin zone center mode and boundary mode are discussed.In particular, we discover a reversal phenomenon of the propagation direction of the Brillouin zone boundary mode.
基金the National Natural Science Foundation of China(No.51775336)the Shanghai Pujiang Program(No.17PJD019)
文摘Mode tracking is required in the structural optimization when the frequencies of certain specified modes must be maintained within a suitable range.A simple tracking method employing the mode number is invalid or misleading when local modes appear or disappear during mesh updating.In this work,a mode tracking scheme combining the nearest neighbor method(NNM)with the modal assurance criterion(MAC)is proposed.Several NNM algorithms are compared,and the k-dimensional tree(kd-tree)NNM is used to transform eigenvectors(mode shapes)from different scales to identical one.A threshold determination method is implemented for the MAC to assess the similarities in all the calculated modes.On the basis of the mode tracking scheme,specified modes can be tracked between different finite element method(FEM)models which have different meshes and optimized shapes.The effectiveness is verified through an example of shape optimization using an electric motor structure.
