Optimization control of central air conditioning based on the improved butterfly optimization algorithm

Abstract This paper presents an optimization methodology for central air conditioning water systems utilizing an enhanced Butterfly Optimization Algorithm (BSGBOA). Initially, the power consumption of components such as cooling water pumps and cooling towers was analyzed in relation to various environmental parameters through regression analysis, culminating in the formulation of a mathematical model. To address the inherent limitations of the traditional Butterfly Optimization Algorithm (BOA), including susceptibility to local optima and suboptimal convergence rates, this study introduces a suite of refinement strategies. These encompass Bernoulli map initialization, adaptive weighting coefficients, sine-cosine optimization tactics, and Gaussian mutation. Collectively, these refinements markedly enhance the algorithm’s global search efficacy and convergence velocity, effectively mitigating the issue of local optima entrapment. Empirical results substantiate that the refined Butterfly Optimization Algorithm (BSGBOA) exhibits pronounced superiority in the realm of central air conditioning system energy consumption optimization, achieving a total energy reduction of 16.98%.on average, and energy efficiency of the system by 16.84%. This substantiates the methodological efficacy of the optimization approach and the algorithmic preeminence. The investigation furnishes a novel technical trajectory for architectural energy conservation and the augmentation of energy utilization efficiency, bearing considerable application merit and developmental potential.