Research On Energy-saving Method Of Central Air-conditioning System Based On Data-driven

After entering the 21 st century,China’s economic development momentum is strong,and it has become the world’s second largest economy.But at the same time of rapid economic development,the energy problem is becoming increasingly prominent in China.According to last year’s global energy usage survey data,China has become one of the largest energy consumption countries.In order to alleviate the energy crisis,the Chinese government has issued policies for many years,putting forward a series of new requirements for sustainable development for the development of the three energy-consuming giants in industry,construction and transportation.Among them,the energy consumption of the construction industry accounts for one-third of the total energy consumption of various industries in the society,and energy-saving solutions for buildings are scarce,which has led to the high energy consumption of the construction industry so far.In the construction industry,the energy consumption of the central air-conditioning system accounts for more than half of the total energy consumption of the building.The initial design requirements of the central air-conditioning system meet the building load under extreme conditions.In actual operation,the air-conditioning system is operated at partial load for more than 90% of the time,which leads to a reduction in the overall efficiency of the system and a large amount of energy waste.In the central air-conditioning system,the cooling water system and the chiller are the two most energy-consuming subsystems,and they are also the two parts with the most energy-saving potential.Therefore,the key to solve the problem of high building energy consumption lies in how to optimize the operation of cooling water system and chillers in the central air-conditioning system.In this paper,a data-driven method is proposed to optimize the internal cooling water system and water chiller of central air conditioning system.Through analyzing the characteristics of the operation of the cooling water system equipment,combined with the data-driven algorithm model,all energy dissipation device in the cooling water system for precise control,namely,cooling water system constraints under the premise of water,through the optimization algorithm of random equipment combination,finally find out a set of equipment start-stop combination,make the system while satisfy the constraints,the minimum energy consumption.For chiller,almost every device is in the state of partial load operation.Therefore,through optimizing the load of each chiller to improve the efficiency of some units,so as to reduce the overall energy consumption of the unit.In the above process,whether for cooling water system or chiller,the pros and cons of the prediction model determines the final energy consumption optimization effect.Therefore,in the stage of establishing the prediction model,different optimization algorithms are used to optimize the training parameters of the regression algorithm,so as to ensure the accuracy of the established model.In this paper,while giving the optimized energy-saving methodology,combined with the actual collected central air-conditioning system operating data,it verifies the feasibility and effectiveness of the method.The optimization results show that the optimized energy-saving scheme proposed in this paper has important practical guiding significance for the energy-saving and emission-reduction of the central air-conditioning system.