[HTML][HTML] Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives
The vigorous expansion of renewable energy as a substitute for fossil energy is the
predominant route of action to achieve worldwide carbon neutrality. However, clean energy …
predominant route of action to achieve worldwide carbon neutrality. However, clean energy …
[HTML][HTML] Machine learning and deep learning in energy systems: A review
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …
and decisive role in all of the sectors of society. To accelerate the process and improve the …
[HTML][HTML] Energy System 4.0: Digitalization of the energy sector with inclination towards sustainability
The United Nations' sustainable development goals have emphasized implementing
sustainability to ensure environmental security for the future. Affordable energy, clean …
sustainability to ensure environmental security for the future. Affordable energy, clean …
[HTML][HTML] Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: A survey of advanced machine learning and deep …
Nowadays, learning-based modeling methods are utilized to build a precise forecast model
for renewable power sources. Computational Intelligence (CI) techniques have been …
for renewable power sources. Computational Intelligence (CI) techniques have been …
[HTML][HTML] Recent advances in energy storage systems for renewable source grid integration: a comprehensive review
MY Worku - Sustainability, 2022 - mdpi.com
The reduction of greenhouse gas emissions and strengthening the security of electric
energy have gained enormous momentum recently. Integrating intermittent renewable …
energy have gained enormous momentum recently. Integrating intermittent renewable …
Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives
Y Guan, D Chaffart, G Liu, Z Tan, D Zhang… - Chemical Engineering …, 2022 - Elsevier
Recently, the availability of extensive catalysis-related data generated by experimental data
and theoretical calculations has promoted the development of machine learning (ML) …
and theoretical calculations has promoted the development of machine learning (ML) …
Advances in machine learning technology for sustainable biofuel production systems in lignocellulosic biorefineries
V Sharma, ML Tsai, CW Chen, PP Sun… - Science of The Total …, 2023 - Elsevier
In view of the global climate change concerns, the society is approaching towards the
development of 'green'and renewable energies for sustainable future. The non-renewable …
development of 'green'and renewable energies for sustainable future. The non-renewable …
Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach
Accurate forecasting of the wave energy is crucial and has significant potential because
every wave meter possesses an energy amount ranging from 30 to 40 kW along the shore …
every wave meter possesses an energy amount ranging from 30 to 40 kW along the shore …
Sustainable energies and machine learning: An organized review of recent applications and challenges
In alignment with the rapid development of artificial intelligence in the era of data
management, the application domains for machine learning have expanded to all …
management, the application domains for machine learning have expanded to all …
Progress of artificial neural networks applications in hydrogen production
The demand for green energy is expanding, and it seems that hydrogen is the best option
that can be produced and stored in large quantities. Hydrogen is a promising energy carrier …
that can be produced and stored in large quantities. Hydrogen is a promising energy carrier …