The“average occupant”methodology is widely used in energy consumption simulations of residential buildings;however,it fails to consider the differences in energy use behavior among family members.Based on a field su...The“average occupant”methodology is widely used in energy consumption simulations of residential buildings;however,it fails to consider the differences in energy use behavior among family members.Based on a field survey on the Central Shaanxi Plain,to identify the energy use behavior patterns of typical families,a stochastic energy use behavior model considering differences in energy use behavior among family members was proposed,to improve the accuracy of energy consumption simulations of residential buildings.The results indicated that the surveyed rural families could be classified into the following four types depending on specific energy use behavior patterns:families of one elderly couple,families of one middle-aged couple,families of one elderly couple and one child,and families of one couple and one child.Moreover,on typical summer days,the results of daily building energy consumption simulation obtained by the“average occupant”methodology were 25.39%and 28%lower than the simulation results obtained by the model proposed in this study for families of one elderly couple and families of one middle-aged couple,and 13.05%and 23.05%higher for families of one elderly couple and one child,and families of one couple and one child.On typical winter days,for the four types of families,the results of daily building energy consumption simulation obtained by the“average occupant”methodology were 21.69%,10.84%,1.21%,and 8.39%lower than the simulation results obtained by the model proposed in this study,respectively.展开更多
The next-generation heating systems,crucial for rational heat distribution and refined management,rely heavily on accurate zone-specific heat load predictions.This paper introduces a method for rapid zone-specific hea...The next-generation heating systems,crucial for rational heat distribution and refined management,rely heavily on accurate zone-specific heat load predictions.This paper introduces a method for rapid zone-specific heat load prediction based on heat consumption allocation and data-driven techniques.The approach involves predicting the overall heat load of the building and then redistributing the total heat according to a heat consumption matrix.This eliminates the need for real-time data collection from each room,resulting in cost savings on hardware and improved computational efficiency.The overall building heat load data is obtained through a data-driven algorithm,while the heat consumption matrix is constructed through energy software simulation analysis.Using Building 2 in the Baotou Industrial Park,China,as a case study,the paper analyzes the differences between actual measurements and room estimates.Experimental results indicate an average error of 7.02%for the proposed estimation method.Although not achieving high precision(>95%)in heat load prediction,this level of accuracy is deemed sufficient to meet the requirements of feedforward control.展开更多
基金funded by the National Natural Science Foundation of China(52378109)Shaanxi Provincial Department of Science and Technology(2023KJXX-043).
文摘The“average occupant”methodology is widely used in energy consumption simulations of residential buildings;however,it fails to consider the differences in energy use behavior among family members.Based on a field survey on the Central Shaanxi Plain,to identify the energy use behavior patterns of typical families,a stochastic energy use behavior model considering differences in energy use behavior among family members was proposed,to improve the accuracy of energy consumption simulations of residential buildings.The results indicated that the surveyed rural families could be classified into the following four types depending on specific energy use behavior patterns:families of one elderly couple,families of one middle-aged couple,families of one elderly couple and one child,and families of one couple and one child.Moreover,on typical summer days,the results of daily building energy consumption simulation obtained by the“average occupant”methodology were 25.39%and 28%lower than the simulation results obtained by the model proposed in this study for families of one elderly couple and families of one middle-aged couple,and 13.05%and 23.05%higher for families of one elderly couple and one child,and families of one couple and one child.On typical winter days,for the four types of families,the results of daily building energy consumption simulation obtained by the“average occupant”methodology were 21.69%,10.84%,1.21%,and 8.39%lower than the simulation results obtained by the model proposed in this study,respectively.
基金supported by the National Natural Science Foundation of China,61765012Natural Science Foundation of Inner Mongolia Autonomous Region,2023MS05047Basic research funds for universities directly under the Inner Mongolia Autonomous Region(2023RCTD011,2023YXXS012)。
文摘The next-generation heating systems,crucial for rational heat distribution and refined management,rely heavily on accurate zone-specific heat load predictions.This paper introduces a method for rapid zone-specific heat load prediction based on heat consumption allocation and data-driven techniques.The approach involves predicting the overall heat load of the building and then redistributing the total heat according to a heat consumption matrix.This eliminates the need for real-time data collection from each room,resulting in cost savings on hardware and improved computational efficiency.The overall building heat load data is obtained through a data-driven algorithm,while the heat consumption matrix is constructed through energy software simulation analysis.Using Building 2 in the Baotou Industrial Park,China,as a case study,the paper analyzes the differences between actual measurements and room estimates.Experimental results indicate an average error of 7.02%for the proposed estimation method.Although not achieving high precision(>95%)in heat load prediction,this level of accuracy is deemed sufficient to meet the requirements of feedforward control.
