Optimal loading distribution of chillers based on an improved beluga whale optimization for reducing energy consumption

A large part of the total energy consumption of building operation comes from chillers system in the building refrigeration system. Optimizing the load distribution of every chiller unit is able to significantly decrease the power consumption of the system. Therefore, this paper proposes an improved beluga whale optimization (IBWO) using multiple strategies addressing the optimal chiller loading (OCL) problems. IBWO incorporates circle chaotic mapping, the efficient search operator of producers of sparrow search algorithm (SSA), and Cauchy mutation strategy to enhance global optimization capability, convergence, and robustness. The enhanced algorithm performance was validated through testing with 6 benchmark functions using MATLAB, demonstrating its improved effectiveness. Additionally, IBWO is applied to the power consumption optimization and load distribution of two typical chiller systems. The results illustrate that compared with the conventional method and other meta-heuristic algorithm, IBWO can provide an energy-saving scheme with excellent robustness, less power consumption and higher overall refrigeration efficiency in a short number of iterations, which preliminarily proves the feasibility for dealing with OCL problems.