Optimal Control Strategy for Variable Air Volume Air-Conditioning Systems Using Genetic Algorithms

Nam-Chul Seong,Wonchang Choi,Jee-Heon Kim

doi:10.3390/su11185122

Abstract

This study is aimed at developing a real-time optimal control strategy for variable air volume (VAV) air-conditioning in a heating, ventilation, and air-conditioning (HVAC) system using genetic algorithms and a simulated large-scale office building. The two selected control variables are the settings for the supply air temperature and the duct static pressure to provide optimal control for the VAV air-conditioning system. Genetic algorithms were employed to calculate the optimal control settings for each control variable. The proposed optimal control conditions were evaluated according to the total energy consumption of the HVAC system based on its component parts (fan, chiller, and cold-water pump). The results confirm that the supply air temperature and duct static pressure change according to the cooling load of the simulated building. Using the proposed optimal control variables, the total energy consumption of the building was reduced up to 5.72% compared to under ‘normal’ settings and conditions.

Highlights

Heating, ventilation, and air-conditioning (HVAC) systems constitute a significant portion of the energy consumption of many buildings [1,2,3]
The optimal control strategy proposed in this study uses an objective function as the energy consumption in a genetic algorithms (GAs)
The proposed optimal control operation was evaluated based on changes in the optimal control variables and energy savings in a simulated HVAC system in a reference large-scale office building

Summary

Introduction

Ventilation, and air-conditioning (HVAC) systems constitute a significant portion of the energy consumption of many buildings [1,2,3]. Researchers have tried to determine a methodology to reduce such HVAC energy consumption [2,4] and, among the developed methodologies, genetic algorithms (GAs) are widely used in various fields and are known to be suitable for solving complex optimization problems, especially when large amounts of data and parameters are involved [5,6]. Extensive research has been conducted to optimize the thermal performance of buildings and reduce energy consumption, that of the building’s HVAC system [8]. Researchers have carried out analyses of the changes in the energy consumption of HVAC systems with respect to building design parameters [9] and have optimized HVAC system design based on simulations [10]

Objectives

Results

Conclusion