Deep Reinforcement Learning for Smart Home Energy Management

TL;DR

This paper presents a deep reinforcement learning approach using DDPG to optimize energy management in smart homes, effectively handling uncertainties without needing thermal models or prior parameter knowledge.

Contribution

It introduces a novel DDPG-based strategy for smart home energy management that operates without thermal models or prior uncertainty information.

Findings

Demonstrates effectiveness on real-world data

Shows robustness against parameter uncertainties

Achieves energy cost minimization

Abstract

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 determine the optimal energy management strategy 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 strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy 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 strategy.