Toward massive machine type communications in ultra-dense cellular IoT networks: Current issues and machine learning-assisted solutions
The ever-increasing number of resource-constrained machine-type communication (MTC)
devices is leading to the critical challenge of fulfilling diverse communication requirements …
devices is leading to the critical challenge of fulfilling diverse communication requirements …
Reinforcement learning in sustainable energy and electric systems: A survey
The dynamic nature of sustainable energy and electric systems can vary significantly along
with the environment and load change, and they represent the features of multivariate, high …
with the environment and load change, and they represent the features of multivariate, high …
Deep reinforcement learning for strategic bidding in electricity markets
Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art
frameworks for modeling strategic bidding decisions in deregulated electricity markets …
frameworks for modeling strategic bidding decisions in deregulated electricity markets …
Reinforcement learning approach for optimal distributed energy management in a microgrid
In this paper, a multiagent-based model is used to study distributed energy management in
a microgrid (MG). The suppliers and consumers of electricity are modeled as autonomous …
a microgrid (MG). The suppliers and consumers of electricity are modeled as autonomous …
Reinforcement learning for electric power system decision and control: Past considerations and perspectives
In this paper, we review past (including very recent) research considerations in using
reinforcement learning (RL) to solve electric power system decision and control problems …
reinforcement learning (RL) to solve electric power system decision and control problems …
Reinforcement learning of heuristic EV fleet charging in a day-ahead electricity market
This paper addresses the problem of defining a day-ahead consumption plan for charging a
fleet of electric vehicles (EVs), and following this plan during operation. A challenge herein …
fleet of electric vehicles (EVs), and following this plan during operation. A challenge herein …
Discrete-Time Deterministic -Learning: A Novel Convergence Analysis
In this paper, a novel discrete-time deterministic Q-learning algorithm is developed. In each
iteration of the developed Q-learning algorithm, the iterative Q function is updated for all the …
iteration of the developed Q-learning algorithm, the iterative Q function is updated for all the …
Strategic behavior of multi-energy players in electricity markets as aggregators of demand side resources using a bi-level approach
M Yazdani-Damavandi, N Neyestani… - … on Power Systems, 2017 - ieeexplore.ieee.org
The coordination of various energy vectors under the concept of multi-energy system (MES)
has introduced new sources of operational flexibility to system managers. In this paper, the …
has introduced new sources of operational flexibility to system managers. In this paper, the …
Agent-based electricity market simulation with demand response from commercial buildings
With the development of power system deregulation and smart metering technologies, price-
based demand response (DR) becomes an alternative solution to improving power system …
based demand response (DR) becomes an alternative solution to improving power system …
Backward Q-learning: The combination of Sarsa algorithm and Q-learning
YH Wang, THS Li, CJ Lin - Engineering Applications of Artificial Intelligence, 2013 - Elsevier
Reinforcement learning (RL) has been applied to many fields and applications, but there are
still some dilemmas between exploration and exploitation strategy for action selection policy …
still some dilemmas between exploration and exploitation strategy for action selection policy …