Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: A survey of advanced machine learning and deep learning …

L Abualigah, RA Zitar, KH Almotairi, AMA Hussein… - Energies, 2022 - mdpi.com
Nowadays, learning-based modeling methods are utilized to build a precise forecast model
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) …

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 …

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 …

Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems

Y Wang, D Qiu, G Strbac - Applied Energy, 2022 - Elsevier
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 …

Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects

Y Li, C Yu, M Shahidehpour, T Yang… - Proceedings of the …, 2023 - ieeexplore.ieee.org
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 …

[HTML][HTML] A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights

BO Abisoye, Y Sun, W Zenghui - Renewable Energy Focus, 2024 - Elsevier
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 …

Integrated risk measurement and control for stochastic energy trading of a wind storage system in electricity markets

D Xiao, H Chen, W Cai, C Wei… - Protection and Control of …, 2023 - ieeexplore.ieee.org
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 …

Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level

H Kang, S Jung, H Kim, J Jeoung, T Hong - Renewable and Sustainable …, 2024 - Elsevier
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 …

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 …