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Liang Yu

Researcher at Nanjing University of Posts and Telecommunications

Publications -  37
Citations -  1531

Liang Yu is an academic researcher from Nanjing University of Posts and Telecommunications. The author has contributed to research in topics: Energy management & Smart grid. The author has an hindex of 19, co-authored 37 publications receiving 877 citations. Previous affiliations of Liang Yu include Huazhong University of Science and Technology.

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Deep Reinforcement Learning for Smart Home Energy Management

TL;DR: In this paper, the authors investigated an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range, and proposed an energy management algorithm based on deep deterministic policy gradients.
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Energy Cost Minimization for Distributed Internet Data Centers in Smart Microgrids Considering Power Outages

TL;DR: This paper forms the problem as a stochastic program that captures service request distribution, server provisioning, energy storage management, generator scheduling, power transactions between smart microgrids, and main grids, and uses the Lyapunov optimization technique to design an operation algorithm.
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Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

TL;DR: In this article, an HVAC control algorithm is proposed to solve the Markov game based on multi-agent deep reinforcement learning with attention mechanism, which does not require any prior knowledge of uncertain parameters and can operate without knowing building thermal dynamics models.
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A Review of Deep Reinforcement Learning for Smart Building Energy Management

TL;DR: A comprehensive review of DRL for SBEM from the perspective of system scale is provided and the existing unresolved issues are identified and possible future research directions are pointed out.
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Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy

TL;DR: An online energy management algorithm based on the framework of Lyapunov optimization techniques without the need to predict any system parameters is proposed to construct and stabilize four queues associated with indoor temperature, electric vehicle charging, and energy storage.