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Vibration Analysis of Driving-Point System with Uncertainties Using Polynomial Chaos Expansion
Published 2020-01-01Get full text
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Nontrivial Solutions of a Fully Fourth-Order Periodic Boundary Value Problem
Published 2014-01-01Get full text
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Solvability of a Fourth-Order Boundary Value Problem with Integral Boundary Conditions
Published 2013-01-01Get full text
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SPMSM Sliding Mode Control Based on the New Super Twisting Algorithm
Published 2021-01-01Get full text
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Existence of Periodic Solutions for Nonlinear Fully Third-Order Differential Equations
Published 2020-01-01Get full text
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Evaluating Visual Satisfaction in Urban Sculpture Spaces: A Multi-Criteria Decision Making Approach
Published 2025-01-01Get full text
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Multivariate Load Forecasting of Integrated Energy System Based on CEEMDAN-CSO-LSTM-MTL
Published 2025-01-01“…Firstly,preprocess the collected raw load data and calculate the actual load value considering system energy loss; Secondly,the maximum information coefficient (MIC) is used to analyze the correlation between multiple loads and between multiple loads and weather factors,and to extract strongly correlated variables of multiple loads; Once again,the strongly correlated variables of multiple loads are substituted into CEEMDAN,and the load data is decomposed into stationary subsequences; Then,the feature sequence is substituted into the LSTM-MTL shared layer and the CSO algorithm is used to optimize the prediction model,achieving collaborative prediction of multiple loads; Finally,the performance of the constructed model was validated using a multivariate load dataset from a chemical park in Jilin City,Jilin Province,China. The results show that compared with traditional prediction models,the constructed model can effectively improve the prediction accuracy of multiple loads in the integrated energy system.…”
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