Analysis Of Influencing Factors Of Heating Room Temperature Based On Regression Analysis

In recent years,with the advancement of urbanization,the service area of the central heating system has been increasing year by year.And with the improvement of people's living standards,people's demand for heating has changed from simply requiring high-temperature heating to more comfortable and personalized room temperature requirements.Correspondingly,in order to meet the needs of residents,heating companies can only continuously improve their own heating levels.As the most direct indicator of heating quality,room temperature faces the problem of being difficult to obtain and is easily affected by many factors.This article aims to use regression analysis methods to study the main factors affecting room temperature,and use the main factors and dependent variables to establish a regression model at room temperature.First,this article processes the real data of a heating company stored in a structured database,and extracts variables for analysis of influencing factors.Secondly,this paper uses the Lasso regression method to screen out the main factors affecting the room temperature,which are the return water temperature,valve opening and outdoor temperature.Then,the main influencing factors and the dependent variable room temperature were used to establish a regression model,and the households with poor regression results were analyzed separately,and the data was processed in sections,and it was found that the households had abnormal values caused by opening windows for a period of time.Finally,after excluding abnormal conditions such as light and window opening,the predicted results of the model and the actual situation are compared and analyzed.The range of the overall accuracy of all households is [0.6,1],and the model is effective in general.