Data-driven Optimal Control of Single Duct VAV Terminal Unit

The purpose of this study is to propose a control method that optimizes the set point of the variable air volume (VAV) terminal unit using real-time prediction model of indoor thermal and air environment and energy consumption. Indoor thermal environment and air environment are predicted through indoor load and carbon dioxide (CO2) concentration. A prediction model was developed through operation data of the VAV terminal unit by artificial neural network (ANN) algorithm. The developed prediction model was used for real-time set point control of the VAV terminal unit. The optimal control of VAV terminal unit can be expressed in 3 step. First, it predicts the current indoor load and CO2 concentration. Second, all supply temperatures and supply air flow rate that can be provided in the predicted condition are repeatedly simulated. Finally, the set point of the minimum energy consumption is applied to the control. The evaluation of the proposed control method was compared with the dual maximum control logic. Comparative evaluation was performed using TRNSYS. It was confirmed that the proposed control method reduced the reheat coil energy consumption by about 20% and the air supply fan energy consumption by about 17% compared to the existing control method.