[HTML][HTML] Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review
Recent years have seen an increasing interest in Demand Response (DR) as a means to
provide flexibility, and hence improve the reliability of energy systems in a cost-effective way …
provide flexibility, and hence improve the reliability of energy systems in a cost-effective way …
[HTML][HTML] Demand-side management in industrial sector: A review of heavy industries
H Golmohamadi - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
The penetration of renewable energies is increasing in power systems all over the world.
The volatility and intermittency of renewable energies pose real challenges to energy …
The volatility and intermittency of renewable energies pose real challenges to energy …
A systematic review on power system resilience from the perspective of generation, network, and load
Power systems are the backbone of modern society, but high-impact and low-probability
natural disasters pose unprecedented challenges to power systems in recent years. Power …
natural disasters pose unprecedented challenges to power systems in recent years. Power …
Machine learning driven smart electric power systems: Current trends and new perspectives
MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020 - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
R Lu, SH Hong - Applied energy, 2019 - Elsevier
Balancing electricity generation and consumption is essential for smoothing the power grids.
Any mismatch between energy supply and demand would increase costs to both the service …
Any mismatch between energy supply and demand would increase costs to both the service …
Reinforcement learning for demand response: A review of algorithms and modeling techniques
JR Vázquez-Canteli, Z Nagy - Applied energy, 2019 - Elsevier
Buildings account for about 40% of the global energy consumption. Renewable energy
resources are one possibility to mitigate the dependence of residential buildings on the …
resources are one possibility to mitigate the dependence of residential buildings on the …
Artificial intelligence enabled demand response: Prospects and challenges in smart grid environment
Demand Response (DR) has gained popularity in recent years as a practical strategy to
increase the sustainability of energy systems while reducing associated costs. Despite this …
increase the sustainability of energy systems while reducing associated costs. Despite this …
[HTML][HTML] Demand response performance and uncertainty: A systematic literature review
The present review has been carried out, resorting to the PRISMA methodology, analyzing
218 published articles. A comprehensive analysis has been conducted regarding the …
218 published articles. A comprehensive analysis has been conducted regarding the …
Machine-learning-based real-time economic dispatch in islanding microgrids in a cloud-edge computing environment
W Dong, Q Yang, W Li… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
The paradigm of the Internet of Things (IoT) and cloud-edge computing plays a significant
role in future smart grids. The data-driven solution integrating the artificial intelligence …
role in future smart grids. The data-driven solution integrating the artificial intelligence …
Real time demand response modeling for residential consumers in smart grid considering renewable energy with deep learning approach
Demand response modelling have paved an important role in smart grid at a greater
perspective. DR analysis exhibits the analysis of scheduling of appliances for an optimal …
perspective. DR analysis exhibits the analysis of scheduling of appliances for an optimal …