Energy Optimization of VAVAC System Using Genetic Algorithms

The temperature of space is maintained at a constant level by establishing a balance between the cooling load generated in the space and the supply air delivered to meet the load. One of the possibilities is to control the system in such a way that it operates at the point of minimum energy requirement while at the same time maintaining the temperature and other comfort requirements. In this paper a methodology has been presented which obtains the energy optimizing set points to control the temperature and volume of the air supplied to the test cells based on the cooling load requirement. The objective of the optimization is to minimize the total power consumed by chiller and fan. Genetic algorithms are employed to optimize the total power function of variable air volume (VAV) system. Binary and floating genetic algorithm for different cooling load has been applied and validated the result by comparing the same with conventional method.