Influence of tribology on global energy consumption, costs and emissions
K Holmberg, A Erdemir - Friction, 2017 - Springer
Calculations of the impact of friction and wear on energy consumption, economic
expenditure, and CO 2 emissions are presented on a global scale. This impact study covers …
expenditure, and CO 2 emissions are presented on a global scale. This impact study covers …
[HTML][HTML] Machine learning methods for wind turbine condition monitoring: A review
This paper reviews the recent literature on machine learning (ML) models that have been
used for condition monitoring in wind turbines (eg blade fault detection or generator …
used for condition monitoring in wind turbines (eg blade fault detection or generator …
A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer
Reliable and accurate wind speed forecasting (WSF) is fundamental for efficient exploitation
of wind power. In particular, high accuracy short-term WSF (ST-WSF) has a significant …
of wind power. In particular, high accuracy short-term WSF (ST-WSF) has a significant …
Variational mode decomposition
During the late 1990s, Huang introduced the algorithm called Empirical Mode
Decomposition, which is widely used today to recursively decompose a signal into different …
Decomposition, which is widely used today to recursively decompose a signal into different …
Carbon price forecasting based on CEEMDAN and LSTM
F Zhou, Z Huang, C Zhang - Applied energy, 2022 - Elsevier
Abstract After signing the Paris Agreement and piloting carbon trading for many years, China
has taken a significant step toward carbon neutrality. Carbon price forecasting is helpful to …
has taken a significant step toward carbon neutrality. Carbon price forecasting is helpful to …
A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction
J **ong, T Peng, Z Tao, C Zhang, S Song, MS Nazir - Energy, 2023 - Elsevier
Accurate wind power forecast is critical to the efficient and safe running of power systems. A
hybrid model that combines complementary ensemble empirical mode decomposition …
hybrid model that combines complementary ensemble empirical mode decomposition …
Removal of artifacts from EEG signals: a review
Electroencephalogram (EEG) plays an important role in identifying brain activity and
behavior. However, the recorded electrical activity always be contaminated with artifacts and …
behavior. However, the recorded electrical activity always be contaminated with artifacts and …
Financial time series forecasting model based on CEEMDAN and LSTM
J Cao, Z Li, J Li - Physica A: Statistical mechanics and its applications, 2019 - Elsevier
In order to improve the accuracy of the stock market prices forecasting, two hybrid
forecasting models are proposed in this paper which combine the two kinds of empirical …
forecasting models are proposed in this paper which combine the two kinds of empirical …
A complete ensemble empirical mode decomposition with adaptive noise
In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is
presented. The key idea on the EEMD relies on averaging the modes obtained by EMD …
presented. The key idea on the EEMD relies on averaging the modes obtained by EMD …
A review on empirical mode decomposition in fault diagnosis of rotating machinery
Rotating machinery covers a broad range of mechanical equipment and plays a significant
role in industrial applications. It generally operates under tough working environment and is …
role in industrial applications. It generally operates under tough working environment and is …