Predictive Model for the Optimized Mixed-Air Temperature of a Single-Duct VAV System

Abstract

As global warming accelerates due to greenhouse gas emissions, more efforts are required to reduce greenhouse gas emissions. One of the methods used to save building energy is the efficient management of building mechanical systems. The economizer control of HVAC systems is an energy-efficient measure that improves operating methods by introducing outdoor air to save cooling energy when the outdoor-air temperature is sufficiently low. When the HVAC system is operated using economizer control, cooling energy can be saved, and the set-point of the mixed-air temperature is kept constant. Several studies are being conducted on the saving of energy using economizers. Although various studies have been conducted on the control of economizers, there is insufficient research dealing with the optimal control of mixed-air temperature in economizers that consider real-time changes. Therefore, in this study, predictive model-based mixed-air temperature optimization for a single-duct VAV system was constructed. For this, an ANN (Artificial Neural Network) that could be analyzed regardless of the variables was applied to predict the load and energy consumption and a simulator was constructed for the optimized mixed air temperature of the system. The predictive model-based control was evaluated in terms of its thermal comfort and energy, along with the existing economizer control. According to the application of the optimal economizer control, the energy consumption of the building was reduced by 28.9% compared to the existing dry-bulb temperature control, and was within ±1 °C of the indoor-air temperature set point.