基于多策略融合灰狼优化算法的纺纱空调能耗优化OA

To reduce the energy consumption of spinning air conditioning systems,a multi-strategy integrated grey wolf optimizer(GWO)was proposed for energy consumption optimization.A model was established to describe the air changes in volume,temperature and humidity in spinning air conditioning system.With the goal of minimizing spinning air conditioning energy consumption,stepless variable frequency fans,water pumps and adjustable air dampers were selected as the decision variables.The workshop temperature and humidity,as well as the preset limits of adjustable equipment were the constraints.A penalty function method was employed to construct the energy consumption optimization model of spinning air conditioning.To solve the above models,a multi-strategy integrated GWO was proposed.To enhance global search capability,Sobol sequence was applied for initialization.The convergence factor was improved by using tanh function.Multiple strategies,such as the moth-flame optimization algorithm,were utilized to update the wolf population,and adaptive t distribution perturbation was introduced to avoid local optima.Through benchmark functions and practical case studies,the algorithm demonstrated strong optimization performance and broad applicability.It is believed that the optimization method can significantly reduce energy consumption in winter,transitional season and summer.