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Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning
In recent years, wind power has emerged as an important source of renewable energy.
When onshore and offshore wind farm regions are connected to the grid for power …
When onshore and offshore wind farm regions are connected to the grid for power …
[HTML][HTML] Overcoming nonlinear dynamics in diabetic retinopathy classification: a robust AI-based model with chaotic swarm intelligence optimization and recurrent long …
Diabetic retinopathy (DR), which is seen in approximately one-third of diabetes patients
worldwide, leads to irreversible vision loss and even blindness if not diagnosed and treated …
worldwide, leads to irreversible vision loss and even blindness if not diagnosed and treated …
Advances in Henry gas solubility optimization: A physics-inspired metaheuristic algorithm with its variants and applications
MA El-Shorbagy, A Bouaouda, HA Nabwey… - IEEE …, 2024 - ieeexplore.ieee.org
The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by
Henry's law, which describes the solubility of the gas in a liquid under specific pressure …
Henry's law, which describes the solubility of the gas in a liquid under specific pressure …
Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids
This paper discusses a tri-layer non-cooperative energy trading approach among multiple
grid-tied multi-energy microgrids (MEMGs) in the restructured integrated energy market. The …
grid-tied multi-energy microgrids (MEMGs) in the restructured integrated energy market. The …
[HTML][HTML] High-pressure supersonic carbon dioxide (CO2) separation benefiting carbon capture, utilisation and storage (CCUS) technology
Carbon capture, utilisation and storage (CCUS) is of unique significance for building a green
and resilient energy system, and it is also a key solution to tackle the climate challenge. The …
and resilient energy system, and it is also a key solution to tackle the climate challenge. The …
DAFA-BiLSTM: Deep autoregression feature augmented bidirectional LSTM network for time series prediction
H Wang, Y Zhang, J Liang, L Liu - Neural Networks, 2023 - Elsevier
Time series forecasting models that use the past information of exogenous or endogenous
sequences to forecast future series play an important role in the real world because most …
sequences to forecast future series play an important role in the real world because most …
A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models
Accurate estimating the state of charge (SOC) can improve battery reliability, safety, and
extend battery service life. The existing battery models used for SOC estimation …
extend battery service life. The existing battery models used for SOC estimation …
Energy balance via memristor synapse in Morris-Lecar two-neuron network with FPGA implementation
Synapses can regulate the energy balance in the neural network. In this work, a two-neuron
network is established by coupling two Morris-Lecar neurons using a memristor synapse …
network is established by coupling two Morris-Lecar neurons using a memristor synapse …
Multi-objective optimization for impeller structure parameters of fuel cell air compressor using linear-based boosting model and reference vector guided evolutionary …
J Fu, H Wang, X Sun, H Bao, X Wang, J Liu - Applied Energy, 2024 - Elsevier
As a pivotal part of cathode air supply system, centrifugal air compressors play a central
position in ensuring efficient operations of onboard fuel cells. To improve the overall …
position in ensuring efficient operations of onboard fuel cells. To improve the overall …
How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning
C Shi, J Zhi, X Yao, H Zhang, Y Yu, Q Zeng, L Li… - Energy, 2023 - Elsevier
This paper studied the carbon peak through the cross-analysis of low-carbon economics
and deep learning. The STIRPAT model and ridge regression was used to distinguish and …
and deep learning. The STIRPAT model and ridge regression was used to distinguish and …