475 citations · 481 across the 4 of their papers we have counts for
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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…
A Review of Deep Reinforcement Learning for Smart Building Energy Management
Liang Yu, Shuqi Qin, Meng Zhang +3
Global buildings account for about 30% of the total energy consumption and carbon emission, raising severe energy and environmental concerns. Therefore, it is significant and urgen…
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…