Energy optimization of air handling unit using CO2 data and coil performance

Air handling unit systems 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 and reduce amount of CO2 gases and energy waste, it is very important for building energy management systems to have an accurate model to help predict and optimize the energy usage and eliminate the energy waste. In this work, two models are described to focus on the energy usage for heating/cooling coils as well as fans for the air handling unit. Enthalpy based effectiveness and Dry Wet coil methods were identified and compared for the system performance. Two different types of control systems were modeled for this research and the results are shown based on occupancy reflected by the collected 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. Air handling unit located in the basement of one campus building is used for the test case of this study. The data for model inputs is collected wirelessly from the building using fully function device (FFD) and pan coordinator to send/receive the data wirelessly. The air handling unit modeling also is done using Engineering Equation Solver EES Software for the coils and AHU subsystems. Current building management system Metasys software was used to get additional data as model inputs. Moving Average technique was utilized to make the model results more readable and less noisy. Simulation results show 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 air handling unit comparing to the other method enthalpy based effectiveness. Also fuzzy logic controller results 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. Air quality 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