Energy Optimization of Air Handling Unit Using CO2 Data and Coil Performance

Abstract

Air handling unit systems (AHU) are the series of mechanical systems that regulate and circulate the air through the ducts inside the buildings. In a commercial setting, air handling units accounted for more than 50% of the total energy cost of the building in 2013. To make the system more energy efficient without compromising comfort, it is very important for building energy management personnel to have tools to monitor the system performance and optimize its operation. Models are needed to meet the needs. The objectives of this study were to (1) develop models for the AHU elements and (2) implement control strategies to improve energy efficiency without sacrificing room comfort based on the published American Society of Heating Refrigeration and Air Conditioning Engineers (ASHRAE) standard. In this study, algorithms were developed to model the energy usage for heating/cooling coils as well as fans for AHU. Enthalpy based effectiveness and Dry Wet coil methods were identified and compared for accuracy of evaluating the system performance. Two different types of control systems were modeled and the results were shown based on occupancy reflected by the collected the rooms’ CO2 data. Discrete On/Off and fuzzy logic controller techniques were simulated using Simulink Matlab software and compared based on energy reduction and system performance. The models were used on an AHU in one of the campus buildings. The data for model inputs were collected wirelessly from the building using fully function devices (FFD) and a pan coordinator to send/receive the data. Current building management system Metasys software was also used to get additional data. The AHU modeling was done using Engineering Equation Solver (EES) Software for the coils and subsystems. Moving Average technique was utilized to process the data. The models were validated by comparing the calculated results with these measured experimentally. Simulation results showed that in humid regions, where there is more than 45% of relative humidity, the dry wet coil method is the effective way to provide more accurate details of the heat transfer and energy usage of the AHU comparing to the enthalpy based effectiveness. Also results of fuzzy logic controller method show that 62% of the current return fan energy can be reduced weekly using this method without sacrificing the occupant comfort level comparing to the ON/OFF method. Energy consumption can be optimized inside the building using fuzzy logic controller. At the same time system performance can be increased by taking the appropriate steps to prevent the loss of static pressure in the ducts. The implementation of the method developed in this study will improve the energy efficiency of the AHU while the occupants comfort level stay intact.

Learning-based CO2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation

“…Discrete On/Off and Fuzzy Logic controller techniques were simulated using Matlab & Simulink, and the results were shown based on occupancy reflected by the collected rooms' CO2 data for energy reduction and system performance. It indicated 62.8% reduction in fan energy consumption using a Fuzzy Logic controller [39]. The investigators [40] have used DCV (demand-controlled ventilation) to change the fan speed according to the change in the room's CO2.…”