Machine learning for a sustainable energy future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …
demands advances—at the materials, devices and systems levels—for the efficient …
Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the …
The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …
autonomous software that optimizes decision-making and energy distribution operations …
Grid integration challenges and solution strategies for solar PV systems: a review
World leaders and scientists have been putting immense efforts into strengthening energy
security and reducing greenhouse gas (GHG) emissions by meeting growing energy …
security and reducing greenhouse gas (GHG) emissions by meeting growing energy …
A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
Microgrids have recently emerged as a building block for smart grids combining distributed
renewable energy sources (RESs), energy storage devices, and load management …
renewable energy sources (RESs), energy storage devices, and load management …
Deep learning for time series forecasting: a survey
Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …
increasing in recent years. Deep neural networks have proved to be powerful and are …
Machine learning technology in biodiesel research: A review
Biodiesel has the potential to significantly contribute to making transportation fuels more
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
Review on deep learning applications in frequency analysis and control of modern power system
The penetration of renewable energy resources (RES) generation and the interconnection of
regional power grids in wide area and large scale have led the modern power system to …
regional power grids in wide area and large scale have led the modern power system to …
[HTML][HTML] Energetics Systems and artificial intelligence: Applications of industry 4.0
Industrial development with the growth, strengthening, stability, technical advancement,
reliability, selection, and dynamic response of the power system is essential. Governments …
reliability, selection, and dynamic response of the power system is essential. Governments …
[HTML][HTML] Artificial intelligence in renewable energy: A comprehensive bibliometric analysis
L Zhang, J Ling, M Lin - Energy Reports, 2022 - Elsevier
In recent years, artificial intelligence methods have been widely applied to solve issues
related to renewable energy because of their ability to solve nonlinear and complex data …
related to renewable energy because of their ability to solve nonlinear and complex data …
[HTML][HTML] Distributed energy systems: A review of classification, technologies, applications, and policies
The sustainable energy transition taking place in the 21st century requires a major
revam** of the energy sector. Improvements are required not only in terms of the …
revam** of the energy sector. Improvements are required not only in terms of the …