Optimal control strategy for HVAC system in building energy management

Heating, ventilating and air conditioning (HVAC) systems have played an important role in building energy and comfort management. It is designed to provide a relatively constant and comfortable temperature in buildings and provide fresh and filtered air with a comfortable humidity level. In this paper, an optimal control strategy is proposed to control the HVAC system for maintaining building's indoor environment with high energy efficiency. The control strategy utilized swarm intelligence to determine the amount of energy dispatched to each equipment in the HVAC system. In order to study the impact of HVAC system operations in the indoor environment, both the building model and HVAC equipment models are developed. A case study is carried out to simulate the real time control process in a specified building environment.

[1]  Leon R. Glicksman,et al.  Thermal and behavioral modeling of occupant-controlled heating, ventilating and air conditioning systems , 1997 .

[2]  Weiwei Liu,et al.  Evaluation program for the energy-saving of variable-air-volume systems , 2007 .

[3]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[4]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[5]  F. W. Yu,et al.  Part load performance of air-cooled centrifugal chillers with variable speed condenser fan control , 2007 .

[6]  Kamel Ghali,et al.  Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm , 2009 .

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  Russell D. Taylor,et al.  SIMULTANEOUS SIMULATION OF BUILDINGS AND MECHANICAL SYSTEMS IN HEAT BALANCE , 1997 .

[9]  Lingfeng Wang,et al.  Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search , 2009, Eng. Appl. Artif. Intell..

[10]  Arnaud G. Malan,et al.  HVAC control strategies to enhance comfort and minimise energy usage , 2001 .

[11]  Xiangjiang Zhou,et al.  Optimal operation of a large cooling system based on an empirical model , 2004 .

[12]  Wayne Turner,et al.  Energy Management Handbook , 2020 .

[13]  Tao Lu,et al.  Estimation of Space Air Change Rates and CO2 Generation Rates for Mechanically-Ventilated Buildings , 2011 .

[14]  K. F. Fong,et al.  HVAC system optimization for energy management by evolutionary programming , 2006 .