Flexible power consumption management using Q learning techniques in a smart home

This paper focuses on applying Q learning techniques in a home energy management agent where the agent learns to find the optimal sequence of turning off appliances so that the appliances with higher priority will not be switched off during peak demand period or power consumption management. The policy based home energy management determines the optimal policy at every instant dynamically by learning through the interaction with the environment using one of the reinforcement learning approaches called Q-learning. The Q-learning home power consumption problem formulation consisting of state space, actions and reward function is presented in this paper. The simulation results show that the proposed Q-learning based power consumption management is very effective and enables the users to have minimum discomfort during participation in peak demand management or at the time when power consumption management is essential when the available power is rationale.