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

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.

The Fellini Museum of Rimini in Italy and the Genetic Algorithms-Based Method to Optimize the Design of an Integrated System Network and Installations

“…GAs have been used for the optimization of a wired data network [16,[56][57][58][59], wireless communication [60][61][62][63], IoT applications [64][65][66][67], electronics circuit [68][69][70], power electronics [71,72], general power distribution [73][74][75][76][77], air conditioning [78][79][80][81], but not specifically for optimizing the design of a wired network, electrical power network, and air conditioning network as a multiple-objectives function of a GA, which represents the scope of the second part of the present paper.…”

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

“…It is because the necessity of energy efficiency, occupant health, and thermal comfort is on the rise. Some studies, such as in [57][58][59], focus on energy savings and lack control of temperature evaluation in battery rooms. Despite the state-of-the-art analysis of the available literature, the study focus is mainly on the residential building which includes offices.…”

“…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.…”

AC Cooling Time Prediction Using Common Representation Model

“…AI solutions to predict building cooling loads in order to reduce AC energy are discussed in [6,7]. An algorithm to control the air flow volume in Air Conditioners is described in [8].…”