Research On Global Energy Saving Optimization Of Central Air Conditioning System

With the development and progress of science and technology,human beings pay more and more attention to the improvement of life quality,at the same time,the problem of energy consumption is increasingly serious.The common problem is that the energy consumption ratio of central air-conditioning is relatively high.The way to achieve a significant energy saving while satisfying the comfort of people's air-conditioned rooms is worthy for us to pay attention to right now.The central air-conditioning is designed usually under the condition of full load,but in practice,central air-conditioning operates under partial load conditions.Because of this,the thermal efficiency of the central air-conditioning can be reduced resulting in energy waste.Thus,it is necessary to study the energy-saving of central air-conditioning.In this thesis,each part of the central air-conditioning system is described in detail and the working principle of the central air-conditioning system is represented.The energy consumption model of the central air-conditioning system is established and analyzed.The energy consumption models include the energy consumption of chillers,chilled water pumps,cooling water pumps,cooling towers and fans.The mutual restraint between each part is analyzed.A constraint condition which is more closer to actual condition is added to the existing constraint condition and the global energy-saving optimization problem of the central air-conditioning system is put forward.The improvement of the particle swarm optimization algorithm in this thesis is realized by adding the inertia weights of the random disturbance sine to adjust it at the early and late stage of the particle swarm search process.In this way,it can achieve a better balance between the global search ability and the local search ability.In order to solve the problem that the global extremum is not easy to converge to the optimal solution in late iteration,Gaussian weighted global extremum is introduced.Three Benchmark functions are selected to verify the effectiveness of the improved particle swarm optimization algorithm.Compared with the simulation results of the ordinary particle swarm optimization algorithm,it can be concluded that the improved particle swarm optimization algorithm has faster convergence rate and higher convergent precision than the ordinary particle swarm optimization.The algorithm proposed in this thesis is much more efficient.The improved particle swarm optimization algorithm presented in this thesis is applied into the global energy-saving optimization of the central air-conditioning system and the optimal operating point corresponding to the minimum energy consumption of the system is confirmed.