4 citations · 7 across the 5 of their papers we have counts for
5 papers
HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Management
Jiadong He, Liang Yu, Zhiqiang Chen +6
This letter proposes an Adversarial Inverse Reinforcement Learning (AIRL)-based energy management method for a smart home, which incorporates an implicit thermal dynamics model. In…
A Review of Hydrogen-Enabled Resilience Enhancement for Multi-Energy Systems
Liang Yu, Haoyu Fang, Goran Strbac +4
Ensuring resilience in multi-energy systems (MESs) has become increasingly urgent and challenging due to the growing frequency and severity of extreme events, such as natural disas…
Optimal Operation of a Hydrogen-based Building Multi-Energy System Based on Deep Reinforcement Learning
Liang Yu, Shuqi Qin, Zhanbo Xu +3
Since hydrogen has many advantages (e.g., free pollution, extensive sources, convenient storage and transportation), hydrogen-based multi-energy systems (HMESs) have received wide…
Deep Reinforcement Learning for Smart Grid Protection Against Coordinated Multistage Transmission Line Attacks
Liang Yu, Zhen Gao, Shuqi Qin +4
With the increase of connectivity in power grid, a cascading failure may be triggered by the failure of a transmission line, which can lead to substantial economic losses and serio…
Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings
Liang Yu, Yi Sun, Zhanbo Xu +4
In commercial buildings, about 40%-50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic bur…