Application And Operation Optimization Method Of Hotel Central Air Conditioning Water System Based On Load Forecast

The energy consumption of the central air-conditioning system of high-level hotels accounts for 30%to 40%of the total energy consumption of its operation stage.The optimization of the operation strategy of the air-conditioning system is an important measure for the hotel to increase energy consumption and increase revenue.As data mining technology is widely used in the field of air conditioning and energy saving,exploring the optimization method and application of the central air conditioning system operation strategy based on data mining has become an important topic of building energy saving.This article predicts the operation load of the hotel air conditioner based on the energy management system of a four-star hotel and optimizes the operation strategy of the air-conditioning water system equipment.The main contents are as follows:Firstly,a method for predicting the operating load of air conditioning systems based on data preprocessing and support vector regression(SVR)is proposed.Among them,the data preprocessing process includes input parameter selection,data cleaning,data integration,data dimensionality reduction and data transformation.Using the training set and test set data in the preprocessing results as input parameters,the grid search and 10-level cross-validation are used to optimize the kernel parameters of the RBF kernel function,and a black box model for the operation load forecast of the hotel air-conditioning system is established.The root mean square error RMSE,goodness of fit R~2 and calculation time T are introduced to evaluate the prediction accuracy,generalization performance and calculation cost of the model.In addition,this paper compares and analyzes whether the main component analysis(PCA)is used to reduce the dimensionality of the input parameters on the load prediction results.The prediction results of the training set and the test set show that the load prediction model has higher prediction accuracy and better generalization performance;using the PCA method for data dimensionality reduction can reduce the calculation cost,but it will reduce the load prediction accuracy.Secondly,the method of combining mechanism and identification was used to establish the gray box mathematical model of energy consumption of chillers and variable frequency pumps and the mechanism model of energy consumption of cooling tower fans.Among them,the energy consumption model of the chiller is a binary function composed of the temperature difference between the inlet temperature of the chiller cooling water and the outlet temperature of the chilled water and the cooling capacity.The energy consumption model of the variable frequency pump is a unitary function composed of the system flow;the energy consumption of the cooling tower fan It is a univariate function composed of the number of fans turned on.Based on the historical operation data of the equipment and the field measured data,the least square method is used to identify the unknown parameters in the gray box model of the energy consumption of the chiller,chilled water pump and cooling water pump,and the fitting results can better reflect the actual project.Then,based on the equipment energy consumption model,the energy saving characteristics of each equipment are analyzed.According to the actual conditions of the project,the qualitative and quantitative analysis of the energy saving characteristics and operation strategies of each equipment under part load is carried out.The results show that lowering the cooling water inlet temperature of the chiller or increasing the cold water outlet temperature at part load can increase the chiller COP,and the increases are respectively 7.55%～29.90%and 4.75%～25.52%.The energy consumption of synchronous speed regulation in the parallel pump speed regulation strategy is the lowest,which is 21.76%less energy than the valve throttle speed regulation.Asynchronous frequency conversion will lead to overload of the cold water pump;The cooling efficiency increases with the increase of the outdoor air wet bulb temperature and the fengshui ratio.When the wet bulb temperature is higher than 28℃,continuing to increase the wind to water ratio has little effect on the cooling efficiency.Finally,the energy consumption optimization function is constructed to optimize the operation strategy of the hotel air-conditioning water system.On the basis of the load prediction results of the hotel air conditioning system and the mathematical model of equipment energy consumption,the energy consumption optimization function of the central air conditioning water system is established.The minimum total energy consumption during the operation of the water system is used as the optimization goal,with the change range of each model parameter and the equipment The heat exchange process is a constraint,and the hotel water system’s equipment operation strategy is optimized on a daily basis and at different load rates.Compared with historical operating data,the total energy consumption of a single-day water system is reduced by 12.11%after optimization.The average values of COP and EER of water chillers are increased by 7.64%and 10.67%,respectively.The energy consumption of water systems at different load rates is reduced by an average of12.40%.The average values of COP and EER of the water system have been increased by 7.85%and 15.28%,respectively.The optimization results verify the feasibility of the optimized control strategy of the air conditioning water system based on load prediction proposed in this paper,which has high application value in the actual energy-saving operation of hotels.