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.