Development of Economizer Control Method with Variable Mixed Air Temperature

Abstract

Achieving energy efficiency by improving the operating method of the system used in existing buildings is attracting considerable attention. The Building Design Criteria for Energy Saving was established to induce energy saving design in the domestic construction field, and the introduction of a free-cooling system, such as an economizer system, as an item of the mechanical sector, was evaluated. The economizer is an energy saving method that reduces the building load by introducing outdoor air through damper control according to the indoor and outdoor conditions. The system consists of dry-bulb temperature control and enthalpy control and the mixed air temperature is kept constant in the conventional economizer controls. On the other hand, in dry-bulb temperature control, when the set value of the mixed air temperature is changed according to the load, additional energy savings are expected compared to the conventional control method. Therefore, this paper proposes an economizer control that makes the mixed air temperature variable according to the load in a Constant Air Volume single duct system. For this, a load prediction is needed and an Artificial Neural Network is used for the load prediction. In addition, the relationship between the mixed air temperature and energy were analyzed using the BIN method and TRNSYS 17. Based on the results of previous analysis, a control method which predicting the load using Artificial Neural Network and controlling the mixed air temperature according to the predicted load in the dry-bulb temperature control of a Constant Air Volume single duct system is proposed and the proposed control was applied to the dynamic simulation program and compared with the conventional control. The results show that the temperature of each room was 21–23 °C in summer and 22.5–26 °C in winter when the economizer was controlled using the proposed control method and the energy consumption analysis showed that 19% of the energy was reduced compared to the conventional method when the proposed method was controlled.

Optimal Economizer Control of VAV System using Machine Learning

“…Lee et al confirmed that the set value of the mixed air temperature that consumes the minimum energy according to the load is not constant when controlling the economizer dry-bulb temperature in constant air volume (CAV) single duct system. Based on this, a control method for resetting the mixed air temperature set value according to the load was proposed [10]. Likewise, many studies on economizer control setpoints are being conducted, but there is a lack of studies on set-points considering real-time weather and HVAC system load conditions.…”

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

“…Lee et al analyzed the relationship between the mixed-air temperature and energy consumption using the BIN method and a simulation; it was confirmed that the set point value of the mixed-air temperature at which energy consumption was minimized was not constant depending on the operating conditions. Accordingly, an optimal economizer control that adjusts the mixed-air temperature variable according to the load using an ANN (Artificial Neural Network) was presented [10] As such, the economizer is an energy-saving operation method, and the mixed air temperature set-point is controlled constantly in the existing control. Although various studies have been conducted on the control of economizers, there is insufficient research dealing with economizer optimal control that control the mixed air temperature set-point in consideration of real-time dynamic conditions (outdoor air condition, indoor occupancy etc.)…”