2381/11495052.v1
Liang Yu
Liang
Yu
Weiwei Xie
Weiwei
Xie
Di Xie
Di
Xie
Yulong Zou
Yulong
Zou
Dengyin Zhang
Dengyin
Zhang
Zhixin Sun
Zhixin
Sun
Linghua Zhang
Linghua
Zhang
Yue Zhang
Yue
Zhang
Tao Jiang
Tao
Jiang
Deep Reinforcement Learning for Smart Home Energy Management
University of Leicester
2020
Smart homes
Energy management
Temperature distribution
Internet of Things
Heuristic algorithms
Load modeling
Deep reinforcement learning
Energy cost
Thermal comfort
Energy storage systems
HVAC systems
2020-03-26 16:26:51
Journal contribution
https://figshare.le.ac.uk/articles/journal_contribution/Deep_Reinforcement_Learning_for_Smart_Home_Energy_Management/11495052
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.