Quo vadis artificial intelligence?
The study of artificial intelligence (AI) has been a continuous endeavor of scientists and
engineers for over 65 years. The simple contention is that human-created machines can do …
engineers for over 65 years. The simple contention is that human-created machines can do …
[HTML][HTML] Applications of reinforcement learning in energy systems
ATD Perera, P Kamalaruban - Renewable and Sustainable Energy …, 2021 - Elsevier
Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …
renewable energy technologies and improve efficiencies, leading to the integration of many …
Applications of artificial intelligence and machine learning in smart cities
Smart cities are aimed to efficiently manage growing urbanization, energy consumption,
maintain a green environment, improve the economic and living standards of their citizens …
maintain a green environment, improve the economic and living standards of their citizens …
[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 …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
Deep reinforcement learning for power system applications: An overview
Due to increasing complexity, uncertainty and data dimensions in power systems,
conventional methods often meet bottlenecks when attempting to solve decision and control …
conventional methods often meet bottlenecks when attempting to solve decision and control …
Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning
C Guo, X Wang, Y Zheng, F Zhang - Energy, 2022 - Elsevier
Microgrid (MG) is an effective way to integrate renewable energy into power system at the
consumer side. In the MG, the energy management system (EMS) is necessary to be …
consumer side. In the MG, the energy management system (EMS) is necessary to be …
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 …
[HTML][HTML] Deep reinforcement learning for energy management in a microgrid with flexible demand
TA Nakabi, P Toivanen - Sustainable Energy, Grids and Networks, 2021 - Elsevier
In this paper, we study the performance of various deep reinforcement learning algorithms to
enhance the energy management system of a microgrid. We propose a novel microgrid …
enhance the energy management system of a microgrid. We propose a novel microgrid …
Control and optimisation of power grids using smart meter data: A review
This paper provides a comprehensive review of the applications of smart meters in the
control and optimisation of power grids to support a smooth energy transition towards the …
control and optimisation of power grids to support a smooth energy transition towards the …