Machine Learning Dynamic Evaluation Model for Energy Consumption of Radiant Air Conditioning in Public Buildings
In order to make radiant air conditioning in public buildings more energy-saving,it is necessary to reasonably evaluate the change of air conditioning energy consumption.Therefore,a machine learning dynamic evaluation model of energy con-sumption for radiant air conditioning in public buildings is proposed.This paper designs a dynamic evaluation model of air con-ditioning energy consumption based on machine learning,and uses a data cleaning algorithm based on mean shift clustering to eliminate abnormal data,retain normal data,input them into KNN filling algorithm,and supplement the missing data points in the air conditioning energy consumption data set to make the data more complete.The processed effective data are transmitted to the Adaboost-BP algorithm.After continuous iteration,the predicted data of air conditioning energy consumption are classi-fied to dynamically evaluate the radiant air conditioning energy consumption in public buildings.Experiments show that the rel-ative error of this model in predicting air conditioning power consumption is kept at 1%and below,which can achieve accurate power consumption analysis,and can effectively predict air conditioning heat load in different seasons,total natural gas con-sumption,carbon dioxide emissions and other energy consumption,so as to achieve a reasonable evaluation of radiant air condi-tioning in public buildings.
public buildingradiant air conditioningmachine learningdynamic evaluationevaluation model