By employing the elastic and elastic plastic finite element method(FEM), the effects of matrix feature on the stress transfer mechanisms of short fiber composites are studied. In the calculation, the variations in ma...By employing the elastic and elastic plastic finite element method(FEM), the effects of matrix feature on the stress transfer mechanisms of short fiber composites are studied. In the calculation, the variations in matrix modulus, yield strength and hardening modulus are considered. It is concluded that large deformation of matrix is harmful to the improvement of the mechanical performances of the composites.展开更多
In power systems,environmental fluctuations and electricity price volatility introduce uncertainties in user energy consumption behaviors,posing significant challenges to reliable energy planning.Existing studies ofte...In power systems,environmental fluctuations and electricity price volatility introduce uncertainties in user energy consumption behaviors,posing significant challenges to reliable energy planning.Existing studies often overlook the coupled relationships between the importance and correlations of multiple complex variables,lack consideration of the weighting and distribution of multi-dimensional features across multi-scale spaces,and fall short in multi-scale extraction and fusion of complex spatiotemporal characteristics.To address these issues,this paper proposes a multi-factor collaborative load forecasting method based on feature importance and multi-scale feature extraction.First,a novel evaluation model integrating feature importance and correlation is developed,and a comprehensive feature importance assessment method is proposed.Then,a multi-dimensional weighting extraction framework is designed,from which a multi-dimensional weight matrix and its multi-layer input structure are constructed.Finally,a multi-scale fusion model driven by a multi-channel convolutional neural network is developed.The backbone network is a multi-channel convolutional structure,consisting of a multilevel feature extraction module in the front,a multi-scale sampling mechanism in the middle,and a multiscale feature fusion architecture in the rear.Based on the proposed comprehensive feature importance assessment method,a multi-factor collaborative load forecasting model is established,achieving accurate load prediction.Experimental results demonstrate that,compared with various state-of-the-art forecasting models,the proposed method reduces Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)by up to 28.30%,24.14%,and 30.35%,respectively.展开更多
文摘By employing the elastic and elastic plastic finite element method(FEM), the effects of matrix feature on the stress transfer mechanisms of short fiber composites are studied. In the calculation, the variations in matrix modulus, yield strength and hardening modulus are considered. It is concluded that large deformation of matrix is harmful to the improvement of the mechanical performances of the composites.
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.62133008)the Key Research and Development Program of Shandong Province(No.2025CXPT076).
文摘In power systems,environmental fluctuations and electricity price volatility introduce uncertainties in user energy consumption behaviors,posing significant challenges to reliable energy planning.Existing studies often overlook the coupled relationships between the importance and correlations of multiple complex variables,lack consideration of the weighting and distribution of multi-dimensional features across multi-scale spaces,and fall short in multi-scale extraction and fusion of complex spatiotemporal characteristics.To address these issues,this paper proposes a multi-factor collaborative load forecasting method based on feature importance and multi-scale feature extraction.First,a novel evaluation model integrating feature importance and correlation is developed,and a comprehensive feature importance assessment method is proposed.Then,a multi-dimensional weighting extraction framework is designed,from which a multi-dimensional weight matrix and its multi-layer input structure are constructed.Finally,a multi-scale fusion model driven by a multi-channel convolutional neural network is developed.The backbone network is a multi-channel convolutional structure,consisting of a multilevel feature extraction module in the front,a multi-scale sampling mechanism in the middle,and a multiscale feature fusion architecture in the rear.Based on the proposed comprehensive feature importance assessment method,a multi-factor collaborative load forecasting model is established,achieving accurate load prediction.Experimental results demonstrate that,compared with various state-of-the-art forecasting models,the proposed method reduces Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)by up to 28.30%,24.14%,and 30.35%,respectively.
