Optimization of Metro Central Air Conditioning Cold Source System Based on PCA-ANN Data Model

Abstract

Due to the unique features of metro central air conditioning systems’ architectural design and application scenarios, systems demand a greater degree of energy-savings than standard buildings. The central air conditioning system is the major energy user in metro stations, with the cooling source system accounting for a substantial portion. As a consequence, enhancing the energy efficiency of the cold source system is critical for optimizing the energy efficiency of the central air conditioning system. After analyzing the potential for energy-savings, we propose an energy-saving control technique for cold source systems based on the PCA-ANN data model. Firstly, an operating condition simulation was performed using operational data and cold source system equipment specifications. The effective operating data in the operational data-base was then filtered using the simulation data. Additionally, principal component analysis was used to examine the chosen dates. Finally, the fitted and calibrated data model was utilized to optimize the functioning of the cold source system. August’s revised approach resulted in a 10.5 percent decrease in system energy consumption. In comparison to using non-optimized energy parameters, the suggested technique provides a variety of energy efficiency advantages.