Model Predictive Control with Adaptive Building Model for Heating Using the Hybrid Air-Conditioning System in a Railway Station

Abstract

A model predictive control (MPC) system with an adaptive building model based on thermal-electrical analogy for the hybrid air conditioning system using the radiant floor and all-air system for heating is proposed in this paper to solve the heating supply control difficulties of the railway station on Tibetan Plateau. The MPC controller applies an off-line method of updating the building model to improve the accuracy of predicting indoor conditions. The control performance of the adaptive MPC is compared with the proportional-integral-derivative (PID) control, as well as an MPC without adaptive model through simulation constructed based on a TRNSYS-MATLAB co-simulation testbed. The results show that the implementation of the adaptive MPC can improve indoor thermal comfort and reduce 22.2% energy consumption compared to the PID control. Compared to the MPC without adaptive model, the adaptive MPC achieves fewer violations of constraints and reduces energy consumption by 11.5% through periodic model updating. This study focuses on the design of a control system to maintain indoor thermal comfort and improve system efficiency. The proposed method could also be applied in other public buildings.

Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse

“…On the other hand, Model Predictive Control (MPC) methods receive considerable interest in research studies, with applications for different economic and industrial sectors thanks to their robustness and ability to model disturbances and nonlinear constraints [49]. In fact, recently, the MPC was extensively used in different kinds of specific applications: vehicle platoon control [50][51][52], in the management of transport flows [53], microgrid energy management [54,55], in chemical processes [56] or in the control of the thermal comfort in indoor environments [57,58].…”

“…Moreover, further approaches may be introduced to improve the performances of the control strategies in terms of scalability and reconfigurability [57]. In this context, the authors in reference [58] proposed a lexicographic multiobjective optimization based on prioritized objectives, while, in reference [74], an improved multiobjective genetic algorithm was adopted to optimize the indoor thermal comfort of buildings.…”

A model predictive control regulation model for radiant air conditioning system based on delay time

“…Charles [33] et al used a cryogenic RAC system as an example to minimize daily energy costs by determining hourly cooling set points through the MPC. Ruixin [34] et al co-simulated a hybrid air conditioning system with radiant floor heating and an all-air air system using TRNSYS and MATLAB. According to simulation results, the suggested adaptive MPC approach saves 22% of energy compared to PID control.…”

An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room

“…Existing literature report intelligent control methods, which include model predictive control (MPC), gain scheduling, optimal control, robust control, nonlinear adaptive control, fuzzy logic, genetic algorithm, etc., [32,34,35,38,[40][41][42][43][44][45][46][47][48][51][52][53][56][57][58][59][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84]. As compared with PIDs as proposed in this study via mixing loops, they are robust and energy efficient.…”