Intelligent comfort management agent for smart residential buildings using an updated Q learning algorithm

Abstract

Development of smart environments is one of the hot researching fields of this digital era. The goal of the presented work is to investigate the applicability of reinforcement learning technique for designing intelligent comfort management systems of smart residences which considers minimising the electricity consumption as its hidden agenda while maintaining maximum comfort of the occupants. Accurate occupancy estimation of a smart homes equipped with ambient sensing is expected to give vital inputs to intelligent appliance scheduling algorithms. The proposed Q learning based intelligent comfort management agent (Q-ICMA) dynamically estimates the occupancy level of the given smart space through ambient sensors embedded in the environment and then utilises this information to drive the environment to the optimum region by automatically controlling the lighting and ventilation systems using Q learning algorithm. Simulation results show that the e updated Q learning based agent achieves the best possible results in terms of maximum rewards and faster convergence in achieving the desired goal state.