Multiagent Reinforcement Learning for Energy Management in Residential Buildings

The aim of this article is to explore the multiagent reinforcement learning approach for residential multicarrier energy management. Defining the multiagents system not only enhances the possibility of dedicating separate demand response programs for different components but also accelerates the computational calculations. We employ the Q-learning to provide the optimum solution in solving the presented residential energy management problem. Furthermore, to address uncertainties, a scenario-based method with the real data and proper probability density functions is used. Deterministic and stochastic numerical calculations are made to justify the effectiveness and robustness of the proposed method. The simulated results indicate that the application of the proposed reinforcement learning-based method leads to lower cost schemes for consumers rather than the conventional optimization-based energy management programs.

Automated summary

This article explores the multiagent reinforcement learning approach for residential multicarrier energy management, employing Q-learning to provide the optimal solution and using a scenario-based method to address uncertainties. The simulated results demonstrate that the proposed method leads to lower cost schemes for consumers compared to traditional optimization-based energy management programs.