Deep Reinforcement Learning for Smart Home Energy Management.pdf (2.88 MB)
Download file

Deep Reinforcement Learning for Smart Home Energy Management

Download (2.88 MB)
journal contribution
posted on 26.03.2020, 16:26 by Liang Yu, Weiwei Xie, Di Xie, Yulong Zou, Dengyin Zhang, Zhixin Sun, Linghua Zhang, Yue Zhang, Tao Jiang
In this paper, we investigate 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. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, non-shiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to design an optimal energy management algorithm for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management algorithm based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed algorithm does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed algorithm.

History

Citation

IEEE Internet of Things, 2019

Author affiliation

Department of Engineering

Version

AM (Accepted Manuscript)

Published in

IEEE Internet of Things

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

eissn

2327-4662

Copyright date

2019

Publisher version

https://ieeexplore.ieee.org/document/8919976/authors#authors

Language

en