Deep Reinforcement Learning for Smart Home Energy Management

By Liang Yu, Weiwei Xie, Di Xie, Yulong Zou, Deng-yin Zhang, Zhixin Sun, Linghua Zhang, Yue Zhang, Tao Jiang. IEEE Internet of Things Journal, 2019

382 citations recorded by the source corpus.

In this article, 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, nonshiftable 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 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. It is worth mentioning that the proposed algorithm does not require the prior knowledge of uncertain parameters and building the thermal dynamics model. The simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed algorithm.

Subfields: Building and Construction, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/b9f8dddd3ef3a3c678a0359eb5c009dbadafcf15

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Na Li
2. Steven H. Low
3. Yujie Tang
4. X. Guan
5. Tao Jiang
6. Xin Chen
7. Guannan Qu
8. Mung Chiang

In Renewable Energy, Sustainability and the Environment

1. Tao Jiang

Citations in the corpus

• A Review of Deep Reinforcement Learning for Smart Building Energy Management (2020, methodology): “[20] Smart home PV, ESS, HVAC Energy cost Thermal comfort DDPG, DNN 8.”
• Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges (2021, methodology): “In [137], the DDPG-based EMS method achieves a relatively stable reward after 3000 episodes in the training process, and reduces the total energy cost by 15 .”