Improved firefly algorithm approach applied to chiller loading for energy conservation

Significant energy savings can be achieved by optimizing chiller operation and design in heating, ventilation and cooling (HVAC) systems. In terms of optimization, various metaheuristics have been proposed to the optimal chiller loading problem. New metaheuristics are also emerging recently, between them the firefly algorithm. Firefly algorithm is a nature inspired algorithm based on the idealized behavior of the flash pattern and characteristics of fireflies. This study proposes a new improved firefly algorithm (IFA) based on Gaussian distribution function to the optimal chiller loading design. To testify the performance of the proposed method, the paper adopts two case studies comparing the results of the developed model using IFA with those of traditional firefly algorithm and other optimization methods in literature. In this paper, the optimization problem is minimize energy consumption of multi-chiller systems, where the objective function is energy consumption and the optimum parameter is the partial loading ratio of each chiller. The results of both case studies show that the proposed IFA outperform several optimization methods of the literature in terms of minimum energy consumption solution of the optimal chiller loading problem.

► Multiple-chiller systems are often applied in air conditioning systems.
► The chiller loading problem is to find a set of chiller output while minimizing a objective function.
► This paper proposes a firefly algorithm to minimize energy consumption in multi-chiller loading.