Study On The Linkage Behavior Of Window Opening And Air Conditioning In Winter In University Dormitories Facing The Street In Northern Guangxi

In the context of modern campus development,the popularity of air conditioning in university dormitories has led to a steady increase in energy consumption.In the southern region,both window ventilation and heating with air conditioning are commonly used during winter.However,existing research has primarily focused on individual window-opening behavior or individual air conditioning usage,lacking a study on the interaction between window opening and air conditioning usage.This gap affects accurate modeling and control of building energy consumption.Therefore,studying the interaction between window opening and air conditioning usage is of significant importance.To understand the window opening behavior,air conditioning usage behavior,influencing factors,and typical patterns in winter dormitories,this study conducted a questionnaire survey among 258 dormitories in five dormitory areas of a university in northern Guangxi.The results showed that in winter,the probabilities of window opening and air conditioning usage were 99.3%and 84.1%respectively.The time periods with the highest probabilities of window opening and air conditioning usage were 11:00～15:00 and23:00～7:00,respectively.The entropy method was used to analyze the main influencing factors of window opening behavior,which were indoor temperature,CO₂ concentration,and outdoor noise.The main influencing factors of air conditioning usage were indoor temperature and daily routine.Based on cluster analysis,four typical window opening behavior patterns and five typical air conditioning usage patterns were identified."Opening during stuffiness"and"closing upon entering"were common window opening habits,while"opening during cold"and"closing upon leaving"were common air conditioning usage habits.Building on the questionnaire survey,eight representative dormitories were selected for on-site monitoring based on the cluster analysis of typical patterns.The monitoring revealed significant differences in indoor environment among different behavior patterns.The average indoor temperature ranged from 14.6°C to 22.7°C,with a difference of 8.1°C.The average indoor CO₂ concentration ranged from 715PPM to 1920PPM,with a difference of 1205PPM.The average noise level was 61.6d B on the street side and 41.6d B on the far street side,with a difference of 20d B.Some dormitories facing the street had excessive noise levels,which inhibited window opening behavior.Based on the analysis of monitoring data from the eight representative dormitories,a conditional trigger action model was used to quantify the window opening and air conditioning behavior patterns and derive an interaction model.The model showed good fit with R² values above 0.85.However,there were significant differences in the trigger thresholds and sensitivities among dormitories with the same behavior patterns.Among the five interaction models obtained,indoor temperature,indoor CO₂ concentration,and entering/exiting events were the main driving forces.The accuracy of the interaction models was validated using the De ST-h software’s human behavior module.The results showed that the daily average window opening time,daily average air conditioning usage time,and simulated air conditioning energy consumption had an MAPE（Mean Absolute Percentage Error）within 10%of the measured values,indicating high accuracy.The models accurately captured the collective behavior of occupants.Based on the existing dormitory thermal environment requirements and air conditioning design standards,the energy-saving potential of dormitories was analyzed using the interaction model.Simulation results showed that by improving the interaction behavior patterns while maintaining the indoor temperature requirement of 16°C during winter in hot-summer/cold-winter regions,the air conditioning energy consumption could be reduced by up to 53.1%,indicating significant energy-saving potential.The outcomes of this research aim to enrich the theory of thermal adaptation behavior and provide theoretical and technical support for university dormitory design,energy consumption modeling,intelligent control,dormitory area road layout,and noise.