Results 61 to 70 of about 406 (163)

Probabilistic forecasting of multivariate loads based on MVMD and dynamic hybrid graph attention network

Zhejiang dianli
Accurate load forecasting is essential for ensuring the economic and reliable operation of integrated energy systems （IESs）. However， the nonstationarity， dynamically time-varying coupling， and high stochasticity of multivariate loads pose significant ...
MAO Junchen, JI Liang, LI Shengwen, CHANG Xiao, LI Botong, LI Zhenkun   +5 more
doaj   +1 more source

Improving Relative Humidity Forecasting Accuracy Using a Hybrid CEEMDAN‐Based Deep Learning and Machine Learning Approach: A Comparative Analysis

Journal of Applied Mathematics, Volume 2026, Issue 1, 2026.
Forecasting relative humidity remains challenging due to its nonlinear, nonstationary characteristics and long‐memory dependence. This study proposes a hybrid decomposition‐ensemble model, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Sample Entropy–Gated Recurrent Unit–Ridge Regression (CEEMDAN‐SE‐GRU‐Ridge), for one‐step‐ahead ...
John Kamwele Mutinda, Tecla Mutave Kyalo, Jackson Ndoto Munyao, Amos Kipkorir Langat, Man Leung Wong   +4 more
wiley   +1 more source

GSVMD: A High‐Performance Method for Denoising Surface‐Electromyography Signals With Generalized Successive Variational Mode Decomposition

IET Signal Processing, Volume 2025, Issue 1, 2025.
Surface electromyography (sEMG) has been used for decades to diagnose movement and neuromuscular disorders; however, sEMG signals are noisy and interfered with, and the nonstationary, nonlinear nature of sEMG signals complicates their use for diagnostic purposes.
Seyyed Ali Zendehbad, Abdollah PourMottaghi, Hamid Reza Kobravi, Elias Mazrooei Rad, Athena Sharifi-Razavi, Zahra Sedaghat, Hadi Dehbovid, Richard Dansereau   +7 more
wiley   +1 more source

SALF: A Self‐Adaptive Learning Framework for Short‐Term Load Forecasting in Smart Grid

International Journal of Energy Research, Volume 2025, Issue 1, 2025.
The energy sector’s rapid expansion necessitates accurate, dependable, and computationally efficient short‐term load forecasting (STLF) models to assure real‐time balance between energy supply and demand. However, the stochastic nature of the energy usage and its reliance on changing weather conditions make accurate forecasting difficult.
Muhammad Sajid Iqbal, Muhammad Adnan, Muhammad Ali Akbar, Amine Bermak, Sharanabasava Ganachari   +4 more
wiley   +1 more source

Multiscale New Energy Price Forecasting Integrating Feature Selection and Parallel Parameter Optimization of IMVMD

International Journal of Energy Research, Volume 2025, Issue 1, 2025.
The article proposes a feature selection framework that integrates principal component analysis (PCA) and random forest (RF) to identify the key factors influencing fluctuations in China’s new energy prices. Based on this, a parallel optimization comparison mechanism is constructed by integrating the enhanced whale optimization algorithm (EWOA ...
JingYe Lyu, Chong Li, Huaiyu Wang
wiley   +1 more source

Motor rolling bearing fault diagnosis based on MVMD energy entropy and GWO-SVM

Journal of Vibroengineering, 2023
For motor rolling bearing fault diagnosis, vibration signal analysis is a common method to extract sensitive fault characteristics. In this paper, a newly signal processing method, multivariate variational mode decomposition (MVMD), is proposed to extract features from motor rolling bearings.
Jian Tang, Qiaoni Zhao
openaire   +1 more source

A Novel Method of Pure Output Modal Identification Based on Multivariate Variational Mode Decomposition

Structural Control and Health Monitoring, Volume 2024, Issue 1, 2024.
This paper proposes a novel parameterized frequency‐domain modal parameter identification method, called direct modal variational mode decomposition (DMVMD), based on the multivariate variational mode decomposition (MVMD) framework and the principle of modal superposition. Under the constraint of normalized mode shapes, this paper theoretically derives
Tao Li, Rui Hou, Kangkang Zheng, Lingfeng Li, Bo Liu, Chia-Ming Chang   +5 more
wiley   +1 more source

A customised 1D-CNN for recognition of freezing of gait in Parkinson’s disease using multivariate decomposition techniques

Open Computer Science
The freezing of gait (FoG) presents a sudden challenge in sustaining movement which becomes a common gait issue in people with later stages of Parkinson’s disease (PD). FoG often results in falls that reduces the individual’s impact on life.
Rajendran Nancy, Selvaraj Thomas G., Pennagaram Krushnakumar Kalkeseetharaman, Ashwala Ravi T., Muthu Sinnasamy Pandian Subathra   +4 more
doaj   +1 more source

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

International Journal of Energy Research, Volume 2024, Issue 1, 2024.
The reliability of a pressurized water reactor power plant’s control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs.
Zhihu Gao, Bo Sun, Zeyu Wu, Yang Xi, Qiang Feng, Zili Wang, Yeli Zhou, Junlin Pan, Jiankun Xu, Chaofan Sun   +9 more
wiley   +1 more source

Bearing Fault Prediction Based on Mixed Domain Features and GWO‐SVM

Journal of Electrical and Computer Engineering, Volume 2024, Issue 1, 2024.
The rotating machinery is composed of rolling bearing connection, so the fault identification of rolling bearing is a very critical task. We propose a bearing fault identification algorithm based on grey wolf optimizer (GWO) to address the common problems of high signal noise, inability of a single indicator to accurately reflect the true state of ...
Xuan Zhou, Ruiyang Xia, Zhaodong Zhang, Sasa Duan, Mao Cheng, Chengjiang Zhou, Min Mao, Antonio J. Marques Cardoso   +7 more
wiley   +1 more source