Thermal comfort control based on MEC algorithm for HVAC systems

This paper combines an efficient reinforcement learning algorithm named Multisamples in Each Cell (MEC) with a building thermal comfort control problem. It implements the efficient exploration rule and makes high use of observed samples. A grid is utilized to partition the continuous state into cells that are used to store samples. A near-upper Q function is obtained based on the samples in each cell. The value iteration technique is designed to derive the near optimal control policy. The algorithm can efficiently balance exploration and exploitation. The entire implementation process needs no model of systems. The thermal comfort criterion, predicted mean vote, is introduced to evaluate zone thermal comfort status. A two story, multi-zone small office building equipped with a variable air volume direct expansion cooling system is built in EnergyPlus to establish an EnergyPlus-MATLAB co-simulation platform. A MEC thermal comfort control simulation is implemented to validate the high performance property compared with Q-learning.