Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: A survey of advanced machine learning and deep learning …
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 …
A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models
A Bhansali, N Narasimhulu, R Pérez de Prado… - Energies, 2023 - mdpi.com
Today, methodologies based on learning models are utilized to generate precise conversion
techniques for renewable sources. The methods based on Computational Intelligence (CI) …
techniques for renewable sources. The methods based on Computational Intelligence (CI) …
Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection
W Huo, W Li, Z Zhang, C Sun, F Zhou… - Energy Conversion and …, 2021 - Elsevier
For optimizing the performance of the proton exchange membrane fuel cells (PEMFCs), the I–
V polarization curve is generally used as an important evaluation metric, which can …
V polarization curve is generally used as an important evaluation metric, which can …
Deep learning for renewable energy forecasting: A taxonomy, and systematic literature review
C Ying, W Wang, J Yu, Q Li, D Yu, J Liu - Journal of Cleaner Production, 2023 - Elsevier
In order to identify power production and demand in realtime for efficient and dependable
management for diverse renewable energy systems, precise and intuitive renewable energy …
management for diverse renewable energy systems, precise and intuitive renewable energy …
Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems
Extreme events are featured by high impact and low probability, which can cause severe
damage to power systems. There has been much research focused on resilience-driven …
damage to power systems. There has been much research focused on resilience-driven …
Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …
more complicated power system with high uncertainty is gradually formed, which brings …
[HTML][HTML] A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights
The efforts to revolutionize electric power generation and produce clean and sustainable
electricity have led to the exploration of renewable energy systems (RES). This form of …
electricity have led to the exploration of renewable energy systems (RES). This form of …
Integrated risk measurement and control for stochastic energy trading of a wind storage system in electricity markets
To facilitate wind energy use and avoid low returns, or even losses in extreme cases, this
paper proposes an integrated risk measurement and control approach to jointly manage …
paper proposes an integrated risk measurement and control approach to jointly manage …
Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level
Installing the battery energy storage system (BESS) and optimizing its schedule to effectively
address the intermittency and volatility of photovoltaic (PV) systems has emerged as a …
address the intermittency and volatility of photovoltaic (PV) systems has emerged as a …
Wind farm control technologies: from classical control to reinforcement learning
H Dong, J Xie, X Zhao - Progress in Energy, 2022 - iopscience.iop.org
Wind power plays a vital role in the global effort towards net zero. A recent figure shows that
93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year …
93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year …