Abstract

This study proposed a prediction model for power consumption using linear programming to optimise return air and supply air temperatures while minimising total power consumption across different time zones. A multiple linear regression training analysis identified the correlation between power consumption and five air-side parameters, namely are the ambient temperature, return air temperature, supply air temperature, and humidity ratios. The results indicated a strong correlation and low root mean squared error, suggesting that these parameters significantly influence power consumption and provide a better-fitting model. From the optimal results of the linear programming model, optimised supply air temperature ranged from 17 °C to 18 °C, with return air temperature consistently at 21 °C, achieving a 4.26% energy saving compared to actual power consumption. In conclusion, the optimised values for return and supply air temperatures can be used to manage air temperature resets for the efficient operation of the air conditioning and mechanical ventilation system.