A survey of artificial neural network in wind energy systems
Wind energy has become one of the most important forms of renewable energy. Wind
energy conversion systems are more sophisticated and new approaches are required based …
energy conversion systems are more sophisticated and new approaches are required based …
Neural network-based fading channel prediction: A comprehensive overview
W Jiang, HD Schotten - IEEE Access, 2019 - ieeexplore.ieee.org
By adapting transmission parameters such as the constellation size, coding rate, and
transmit power to instantaneous channel conditions, adaptive wireless communications can …
transmit power to instantaneous channel conditions, adaptive wireless communications can …
Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons
With the rapid advancement of the high-performance computing technology and the
increasing availability of the mass-storage memory device, the application of the data-driven …
increasing availability of the mass-storage memory device, the application of the data-driven …
Learning a transferable change rule from a recurrent neural network for land cover change detection
When exploited in remote sensing analysis, a reliable change rule with transfer ability can
detect changes accurately and be applied widely. However, in practice, the complexity of …
detect changes accurately and be applied widely. However, in practice, the complexity of …
Charging demand of plug-in electric vehicles: Forecasting travel behavior based on a novel rough artificial neural network approach
The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy
saving and environmental benefits. In order to address PEVs impact on the electric …
saving and environmental benefits. In order to address PEVs impact on the electric …
Dual-branch difference amplification graph convolutional network for hyperspectral image change detection
Hyperspectral image (HSI) change detection aims to identify the differences in multitemporal
HSIs. Recently, a graph convolutional network (GCN) has attracted increasing attention in …
HSIs. Recently, a graph convolutional network (GCN) has attracted increasing attention in …
Size optimization and sensitivity analysis of hybrid wind/PV micro-grids-a case study for Bangladesh
This paper presents a feasibility and sensitivity analysis of renewable energy-based off-grid
and grid-connected microgrids by investigating the potentials of wind and solar energy at …
and grid-connected microgrids by investigating the potentials of wind and solar energy at …
Jellyfish optimized recurrent neural network for state of health estimation of lithium-ion batteries
The state of health (SOH) of lithium-ion batteries is considered an important health indicator
to evaluate different features such as efficiency, robustness, and accuracy. The SOH of the …
to evaluate different features such as efficiency, robustness, and accuracy. The SOH of the …
Review of recent control strategies for the traction converters in high-speed train
Electric means of transportation have seen a rapid expansion partly due to flexibility to
commuters and carbon emission reduction. However, transportation electrification is …
commuters and carbon emission reduction. However, transportation electrification is …
Control of Single-Phase Grid-Connected Converters With LCL Filters Using Recurrent Neural Network and Conventional Control Methods
Single-phase grid-connected inverters are widely used to connect small-scale distributed
renewable resources to the grid. However, unlike a three-phase system, control for a single …
renewable resources to the grid. However, unlike a three-phase system, control for a single …