Towards Physics-Informed Machine Learning-Based Predictive Maintenance for Power Converters–A Review
Predictive maintenance for power electronic converters has emerged as a critical area of
research and development. With the rapid advancements in deep-learning techniques, new …
research and development. With the rapid advancements in deep-learning techniques, new …
Leveraging the machine learning techniques for demand-side flexibility–A comprehensive review
The increasing reliance on renewable energy sources poses challenges in managing the
grid, necessitating a focus on energy efficiency and grid stability for a smooth energy …
grid, necessitating a focus on energy efficiency and grid stability for a smooth energy …
[PDF][PDF] Projection-Optimal Monotonic Value Function Factorization in Multi-Agent Reinforcement Learning.
Reinforcement learning has demonstrated its capability to solve challenging real-world
problems, ranging from autonomous driving to robotics and planning [1–12]. In some …
problems, ranging from autonomous driving to robotics and planning [1–12]. In some …
A deep residual reinforcement learning algorithm based on Soft Actor-Critic for autonomous navigation
S Wen, Y Shu, A Rad, Z Wen, Z Guo, S Gong - Expert Systems with …, 2025 - Elsevier
The problem of autonomous navigation has attracted significant attention from robotics
research community in the last few decades. In this paper, we address the problem of low …
research community in the last few decades. In this paper, we address the problem of low …
[HTML][HTML] Integrating renewable energy and plug-in electric vehicles into security constrained unit commitment for hybrid power systems
Nowadays, the demand for power supply is increasing day by day due to industrialization,
population growth, and civilization. Therefore, it is crucial to meet this rising demand by …
population growth, and civilization. Therefore, it is crucial to meet this rising demand by …
Vflh: A following-the-leader-history based algorithm for adaptive online convex optimization with stochastic constraints
This paper considers online convex optimization (OCO) with generated iid stochastic
constraints, where the performance is measured by adaptive regret. The stochastic …
constraints, where the performance is measured by adaptive regret. The stochastic …
The development of four efficient optimal neural network methods in forecasting shallow foundation's bearing capacity
H Moayedi, BN Le - Computers and Concrete, 2024 - koreascience.kr
This research aimed to appraise the effectiveness of four optimization approaches-cuckoo
optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization …
optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization …
[HTML][HTML] Probabilistic load forecasting for integrated energy systems using attentive quantile regression temporal convolutional network
The burgeoning proliferation of integrated energy systems has fostered an unprecedented
degree of coupling among various energy streams, thereby elevating the necessity for …
degree of coupling among various energy streams, thereby elevating the necessity for …
Two-timescale online coordinated schedule of active distribution network considering dynamic network reconfiguration via bi-level safe deep reinforcement learning
L Xue, J Wang, Y Qin, Y Zhang, Q Yang, Z Li - Electric Power Systems …, 2024 - Elsevier
Mass access of renewable energy leads to high voltage fluctuations and network loss. To
coordinate different scheduling resources in different timescales while improving voltage …
coordinate different scheduling resources in different timescales while improving voltage …
Evolution-Assisted Deep Reinforcement Learning for Fast Charging Station Coordinated Operation
The shift towards transportation electrification, marked by the rising use of electric vehicles
(EVs) and the development of fast charging stations (FCS), plays a crucial role in transport …
(EVs) and the development of fast charging stations (FCS), plays a crucial role in transport …