Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience,efficiency,and security of urban/building energy systems while safeguarding occupant comfort.However,data refle...Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience,efficiency,and security of urban/building energy systems while safeguarding occupant comfort.However,data reflecting such behavior are often considered as outliers,and removed or smoothed during preprocessing,limiting insights into their potential impacts.This paper proposes an abnormal behavior analysis method that identifies outliers(considering data distribution)and anomalies(considering the physical context)based on the statistical principle and domain knowledge,assessing their effects on energy supply security.A 4-quadrant graph is proposed to quantify and categorize the impacts of buildings on urban energy systems.The method is illustrated by data from 1,451 buildings in a city.Results show that the proposed method can identify abnormal data effectively.Buildings in the primary industry have more outliers,while those in the tertiary industry have more anomalies.Seven buildings affecting both the security and economy of urban energy systems are identified.The outliers rise more frequently from 8:00 to 18:00,on weekdays and in the summer and winter months.However,the anomaly distribution has a weak connection with time.Moreover,the abnormal electricity usage behavior positively correlates with outdoor air temperatures.This method provides a new perspective for identifying potential risks,managing energy usage behavior,and enhancing flexibility of the urban energy systems.展开更多
Along with the improvement of social productivity and living standard,residential buildings generate a growing portion of carbon emissions,especially during the operation stage.However,energy use behaviors are usually...Along with the improvement of social productivity and living standard,residential buildings generate a growing portion of carbon emissions,especially during the operation stage.However,energy use behaviors are usually ignored in carbon emission calculation.This study focuses on calculating carbon emissions during the operation stage for residential buildings based on the characteristics of energy use behaviors in different regions.Firstly,we investigated energy use behaviors in dwellings across three cities in China:Xi'an,Shanghai and Fuzhou.Then,we established calibrated carbon emission models and optimization models with different green building measures for residential buildings.The results of this research reveal a significant disparity between the energy usage habits of residents in different climate regions.The carbon emissions of residential electricity bills in Xi'an,Shanghai and Fuzhou are 13.6 kgCO_(2)/(m^(2)·a)(excluding central heating),29.3 kgCO_(2)/(m^(2)·a)and 17.2 kgCO_(2)/(m^(2)·a),respectively.Equipment carbon emissions account for 32.2%-64.1% of the total.In comparison to the model based on internal standard setting,the accuracy of the models using actual internal has improved by 25.9%-37.4%.The three-star green building methods have the highest carbon reduction rate among different star buildings,the emission reduction rates are around 30%.This study's findings are useful for carbon emission calculation and green building design of residential buildings in the future.展开更多
Occupant behavior largely influence the energy use within buildings.In the multi-occupant office,occupant behavior is affected by individual preference as well as the interaction among occupants,and yet no suitable mo...Occupant behavior largely influence the energy use within buildings.In the multi-occupant office,occupant behavior is affected by individual preference as well as the interaction among occupants,and yet no suitable model is available to precisely reflect the behavior characteristics.This paper proposed and introduced a method for innovative multi-occupant air-conditioning(AC)usage behavior modelling in a multi-occupant office,which used intuitionistic fuzzy preference relationship to describe individual behavior intention and a hierarchical structure to reflect the social relationship among multiple occupants through subjective evaluation method.The group decision-making process combined the individual behavior intention and the weights of occupants using the analytic hierarchy process.Then,the AC usage behavior of a multi-occupant office was simulated by integrating the multi-occupant model into designer’s simulation toolkit(DeST)building performance simulation software.The results of conducted analysis of a single office with multi-occupant showed that the proposed multi-occupant modelling method could quantitatively characterize the group relationships and AC usage behavior patterns.The absolute errors for the total AC operation time and frequency of the start-up periods of AC between the simulation and measurement results were only 2.7%and 2.0%,respectively.Thus,the proposed multi-occupant modelling method could realize a relatively accurate simulation of the multi-occupant behavior.展开更多
A model-based optimal dispatch framework was proposed to optimize operation of residential flexible loads considering their real-life operating characteristics,energy-related occupant behavior,and the benefits of diff...A model-based optimal dispatch framework was proposed to optimize operation of residential flexible loads considering their real-life operating characteristics,energy-related occupant behavior,and the benefits of different stakeholders.A pilot test was conducted for a typical household.According to the monitored appliance-level data,operating characteristics of flexible loads were identified and the models of these flexible loads were developed using multiple linear regression and K-means clustering methods.Moreover,a data-mining approach was developed to extract the occupant energy usage behavior of various flexible loads from the monitored data.Occupant behavior of appliance usage,such as daily turn-on times,turn-on moment,duration of each operation,preference of temperature setting,and flexibility window,were determined by the developed data-mining approach.Based on the established flexible load models and the identified occupant energy usage behavior,a many-objective nonlinear optimal dispatch model was developed aiming at minimizing daily electricity costs,occupants’dissatisfaction,CO_(2) emissions,and the average ramping index of household power profiles.The model was solved with the assistance of the NSGA-III and TOPSIS methods.Results indicate that the proposed framework can effectively optimize the operation of household flexible loads.Compared with the benchmark,the daily electricity costs,CO_(2) emissions,and average ramping index of household power profiles of the optimal plan were reduced by 7.3%,6.5%,and 14.4%,respectively,under the TOU tariff,while those were decreased by 9.5%,8.8%,and 23.8%,respectively,under the dynamic price tariff.The outputs of this work can offer guidance for the day-ahead optimal scheduling of household flexible loads in practice.展开更多
基金funded by the program Research and Application of Demand Response Potential Evaluation Technologies Based on Massive Electricity Data(No.B31532238944)supported by the State Grid Hubei Electric Power Research Institute.
文摘Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience,efficiency,and security of urban/building energy systems while safeguarding occupant comfort.However,data reflecting such behavior are often considered as outliers,and removed or smoothed during preprocessing,limiting insights into their potential impacts.This paper proposes an abnormal behavior analysis method that identifies outliers(considering data distribution)and anomalies(considering the physical context)based on the statistical principle and domain knowledge,assessing their effects on energy supply security.A 4-quadrant graph is proposed to quantify and categorize the impacts of buildings on urban energy systems.The method is illustrated by data from 1,451 buildings in a city.Results show that the proposed method can identify abnormal data effectively.Buildings in the primary industry have more outliers,while those in the tertiary industry have more anomalies.Seven buildings affecting both the security and economy of urban energy systems are identified.The outliers rise more frequently from 8:00 to 18:00,on weekdays and in the summer and winter months.However,the anomaly distribution has a weak connection with time.Moreover,the abnormal electricity usage behavior positively correlates with outdoor air temperatures.This method provides a new perspective for identifying potential risks,managing energy usage behavior,and enhancing flexibility of the urban energy systems.
基金funded by the Youth Program of the National Natural Science Foundation of China(No.51908006)supported by Beijing lemon tree green building technology CO.,LTD.
文摘Along with the improvement of social productivity and living standard,residential buildings generate a growing portion of carbon emissions,especially during the operation stage.However,energy use behaviors are usually ignored in carbon emission calculation.This study focuses on calculating carbon emissions during the operation stage for residential buildings based on the characteristics of energy use behaviors in different regions.Firstly,we investigated energy use behaviors in dwellings across three cities in China:Xi'an,Shanghai and Fuzhou.Then,we established calibrated carbon emission models and optimization models with different green building measures for residential buildings.The results of this research reveal a significant disparity between the energy usage habits of residents in different climate regions.The carbon emissions of residential electricity bills in Xi'an,Shanghai and Fuzhou are 13.6 kgCO_(2)/(m^(2)·a)(excluding central heating),29.3 kgCO_(2)/(m^(2)·a)and 17.2 kgCO_(2)/(m^(2)·a),respectively.Equipment carbon emissions account for 32.2%-64.1% of the total.In comparison to the model based on internal standard setting,the accuracy of the models using actual internal has improved by 25.9%-37.4%.The three-star green building methods have the highest carbon reduction rate among different star buildings,the emission reduction rates are around 30%.This study's findings are useful for carbon emission calculation and green building design of residential buildings in the future.
基金This study was supported by the National Natural Science Founda-tion of China(Grant no.51978481)。
文摘Occupant behavior largely influence the energy use within buildings.In the multi-occupant office,occupant behavior is affected by individual preference as well as the interaction among occupants,and yet no suitable model is available to precisely reflect the behavior characteristics.This paper proposed and introduced a method for innovative multi-occupant air-conditioning(AC)usage behavior modelling in a multi-occupant office,which used intuitionistic fuzzy preference relationship to describe individual behavior intention and a hierarchical structure to reflect the social relationship among multiple occupants through subjective evaluation method.The group decision-making process combined the individual behavior intention and the weights of occupants using the analytic hierarchy process.Then,the AC usage behavior of a multi-occupant office was simulated by integrating the multi-occupant model into designer’s simulation toolkit(DeST)building performance simulation software.The results of conducted analysis of a single office with multi-occupant showed that the proposed multi-occupant modelling method could quantitatively characterize the group relationships and AC usage behavior patterns.The absolute errors for the total AC operation time and frequency of the start-up periods of AC between the simulation and measurement results were only 2.7%and 2.0%,respectively.Thus,the proposed multi-occupant modelling method could realize a relatively accurate simulation of the multi-occupant behavior.
基金This work was supported by the National Natural Science Foundation of China(52278104)the Science and Technology Innovation Program of Hunan Province(2017XK2015).
文摘A model-based optimal dispatch framework was proposed to optimize operation of residential flexible loads considering their real-life operating characteristics,energy-related occupant behavior,and the benefits of different stakeholders.A pilot test was conducted for a typical household.According to the monitored appliance-level data,operating characteristics of flexible loads were identified and the models of these flexible loads were developed using multiple linear regression and K-means clustering methods.Moreover,a data-mining approach was developed to extract the occupant energy usage behavior of various flexible loads from the monitored data.Occupant behavior of appliance usage,such as daily turn-on times,turn-on moment,duration of each operation,preference of temperature setting,and flexibility window,were determined by the developed data-mining approach.Based on the established flexible load models and the identified occupant energy usage behavior,a many-objective nonlinear optimal dispatch model was developed aiming at minimizing daily electricity costs,occupants’dissatisfaction,CO_(2) emissions,and the average ramping index of household power profiles.The model was solved with the assistance of the NSGA-III and TOPSIS methods.Results indicate that the proposed framework can effectively optimize the operation of household flexible loads.Compared with the benchmark,the daily electricity costs,CO_(2) emissions,and average ramping index of household power profiles of the optimal plan were reduced by 7.3%,6.5%,and 14.4%,respectively,under the TOU tariff,while those were decreased by 9.5%,8.8%,and 23.8%,respectively,under the dynamic price tariff.The outputs of this work can offer guidance for the day-ahead optimal scheduling of household flexible loads in practice.
