Energy Saving by Reinforcement Learning for Multi-Chillers of HVAC Systems

Abstract

This paper presents a method for controlling and operating a multi-chillers system: (1) Model-based control approach was used by MATLAB/SIMULINK to model a building containing two non-identical chillers depending on thermal loads. (2) ON/OFF all chillers alternately using the model reinforcement learning controller (RL-control) to select the appropriate chiller for the building conditioning process. The results were in terms of energy efficiency and performance of the enhanced learning control for the chiller, and a control unit signal (PID) was applied to make a comparison with the signals of energy, power, and temperatures. After comparison, it was found that the energy saving through the proposed controller is 45% of the traditional (PID) strategy, where can the proposed strategy control for the chiller appropriate for the building's conditioning process.

Deep clustering of cooperative multi-agent reinforcement learning to optimize multi chiller HVAC systems for smart buildings energy management

“…Some researchers are intended to address these limitations by implementing the model predictive control (MPC) [18]. Although the PMC is robust to both disturbances and time-varying parameters and coefficient of performance (COP) improvements, it needs to identify the system's proper model, which can be challenging [19]. Thus, the MPC involves a complex model to get high-quality nonlinear model fit and expends a lot of time for more calculations [20].…”