By studying a cluster model containing Ni region （phase）, NiaAI region （phase） and Ni/Ni3Al region （interface） with a first-principles method, the occupation behavior and the ductility effect of zirconium in a N...By studying a cluster model containing Ni region （phase）, NiaAI region （phase） and Ni/Ni3Al region （interface） with a first-principles method, the occupation behavior and the ductility effect of zirconium in a Ni-Ni3Al system were investigated. It is found that zirconium has a stronger segregation tendency to Ni region than to Ni3Al region. The bond order analyses based on Rice-Wang model and the maximum theoretical shear stress model, however, show that zirconium has different degrees of ductility effect in these three regions, which originates from its different ability to increase the Griffith work of interracial cleavage 2γint and to decrease the maximum theoretical shear stress τmax. In addition, it is revealed in this paper that the distinct behavior of zirconium from boron to restrain hydrogen-induced embrittlement can be attributed to their different influences on the crystalline and electronic structures in Ni-Ni3Al alloys.展开更多
The influence of lattice misfit on the occupation behavior and the ductility effect of Zr in Ni-Ni3Al alloys were explored. It is found in energy analysis that the preferable site of Zr between Ni sublattice and Al su...The influence of lattice misfit on the occupation behavior and the ductility effect of Zr in Ni-Ni3Al alloys were explored. It is found in energy analysis that the preferable site of Zr between Ni sublattice and Al sublattice will change under different lattice misfit, however, the Zr prefers to segregate Ni phase rather than Ni3Al phase in all lattice misfit range, which makes it impossible for Zr to go into Ni3Al phase to occupy Al sublattice in Ni-Ni3Al system. Bond order （BO） analysis shows that the localized ductility effect of Zr differs in different region, and the comparison between Zr-free and Zr-doped BO analysis successfully explain the mechanism of the embrittlement of Ni-Ni3Al alloys and the ductility effect of Zr.展开更多
This work explores three patterns of occupants’ control of window blinds and the potential influence on daylight performance of an office room in a tropical climate. In this climate, windows are frequently obstructe...This work explores three patterns of occupants’ control of window blinds and the potential influence on daylight performance of an office room in a tropical climate. In this climate, windows are frequently obstructed by curtains to avoid glare, despite the daylighting and the exterior view. The consequences are obstructed outside view, poor daylight quality and dependency on artificial lighting. This paper assesses the impact on available daylight using parametric analysis based on daylighting dynamic computer simulations using Grasshopper and Daysim software, combining WWR （window-to-wall ratio） （40% and 80%）, SVF （sky view factor） （small and large） and occupant behavior （active, intermediate and passive users）. The user patterns are based in an office buildings survey that identifies preferences concerning daylight use and control of shading devices. The daylight performance criteria combine UDI （useful daylight illuminance） （500-5,000 lux） and illuminance uniformity distribution. Results confirm the impact of occupant behavior on daylighting performance. The optimum combination of external shading devices, high SVF and high window size results in a useful daylighting for 1/3 of the time for passive users and 2/3 for active users.展开更多
Existing studies show that occupants’behavior contribute to fluctuations in energy consumption of residential units within the same building configuration.Window blinds are one of the interior design elements that th...Existing studies show that occupants’behavior contribute to fluctuations in energy consumption of residential units within the same building configuration.Window blinds are one of the interior design elements that the occupants use to control indoor environmental conditions.The way that occupants adjust their blinds could affect the energy performance of buildings.Thus,the purpose of this research was to identify spatial and temporal explanatory variables that correlate with occupants’use of the blinds and determine whether those variables relate to building design and surround-ing sites.Data were collected by observing how occupants in apartment buildings located in a multifamily residential complex adjust their blinds.Descriptive statistics were used to define the effect of floor level,window orientation,day of observation,the hour of observation,and weather conditions on the blind status.In addition,a generalized linear mixed model was used to predict the effect of floor level and window orientation on the occupants’adjustment of blinds.The results revealed that occupants’use of the blinds correlated significantly with spatial factors,such as the apartment buildings’floor level and windows’orientation.Interesting blind use patterns were related to temporal factors,such as the day and hour of observation.展开更多
Building Performance Simulation(BPS)has been adopted as an essential tool for designing,operating,and retrofitting buildings to optimize energy efficiency throughout the building life cycle.The Green Building XML(gbXM...Building Performance Simulation(BPS)has been adopted as an essential tool for designing,operating,and retrofitting buildings to optimize energy efficiency throughout the building life cycle.The Green Building XML(gbXML)schema facilitates seamless data exchange between Building Information Modeling(BIM)and Building Energy Modeling(BEM)software tools.However,limited occupant behavior(OB)representation in BIM often leads to inconsistent and inaccurate energy simulation in BEM software.This paper presents 154 systematic enhancements to the existing occupant behavior XML(obXML)schema v1.3.4,initially developed for standardizing OB representation for BEM,to address existing limitations and improve interoperability with BIM models.The enhancements encompass improved integration with BIM models through extended building representations and system operations,expanded support for advanced OB models with additional environmental parameters and mathematical capabilities,and implementation of a standardized model documentation framework.To facilitate seamless data transformation between gbXML and obXML schemas,we developed a publicly available gb-obXML Schema Converter.Three case studies demonstrate the enhanced schema’s capabilities:representation of building information using a two-story office building model,documentation of a window operation behavior model,and validation of the schema converter’s functionality.The enhanced obXML schema v1.4 enables sophisticated modeling of occupant-building interactions while maintaining consistency with industry-standard BIM schemas.The standardized documentation framework facilitates reproducibility and knowledge sharing in the OB research community,while the schema converter automates the integration of building information into OB simulation workflows.These enhancements establish a foundation for more accurate building performance simulation by supporting sophisticated representation of occupant behavior within the BIM-to-BEM simulation workflows.展开更多
Occupant behavior is an important factor affecting building energy consumption.Many studies have been conducted recently to model occupant behavior and analyze its impact on building energy use.However,to achieve a re...Occupant behavior is an important factor affecting building energy consumption.Many studies have been conducted recently to model occupant behavior and analyze its impact on building energy use.However,to achieve a reduction of energy consumption in buildings,the coordination between occupant behavior and energy-efficient technologies are essential to be considered simultaneously rather than separately considering the development of technologies and the analysis of occupant behavior.It is important to utilize energy-efficient technologies to guide the occupants to avoid unnecessary energy uses.This study,therefore,proposes a new concept,“technology-guided occupant behavior”to coordinate occupant behavior with energy-efficient technologies for building energy controls.The occupants are involved into the control loop of central air-conditioning systems by actively responding to their cooling needs.On-site tests are conducted in a Hong Kong campus building to analyze the performance of“technology-guided occupant behavior”on building energy use.According to the measured data,the occupant behavior guided by the technology could achieve“cooling on demand”principle and hence reduce the energy consumption of central air-conditioning system in the test building about 23.5%,which accounts for about 7.8%of total building electricity use.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
Simulation is recognized as an effective tool for building energy performance assessment during design or retrofit processes. Nevertheless, simulation models yield deviating outcomes from the actual building performan...Simulation is recognized as an effective tool for building energy performance assessment during design or retrofit processes. Nevertheless, simulation models yield deviating outcomes from the actual building performance and a significant portion of this deviation originates from the dynamic nature of occupant behavior. Literature on occupant behavior indicates that occupant behavior is not integrated into building energy performance assessment procedures with appropriate resolution, instead they are accepted as assumed and fixed data sets that usually represent the presence of occupants. This study attempts to evaluate the effect of diverse patterns of occupant behavior on energy performance simulation for office buildings. Diverse levels of sensitivity of occupant behavior on control-based activities such as using lighting apparatus, adjusting thermostat settings, and presence in space are employed through three diverse occupant behavior patterns. These occupancy patterns are correlated with three identical office spaces simulated within a conceptual office building. EDSL Tas is used to run building energy performance simulations. Effects of occupant behavior patterns on simulation outcomes are compared for five sample winter and summer workdays, with respect to heating and cooling loads. Results present findings on how diversity of occupancy profiles influences the consumption outcomes.展开更多
Occupant behavior(OB)is one of the significant sources of uncertainty in building performance simulation.While OB modeling has received increased attention in the past decade,research on the degree of granularity or l...Occupant behavior(OB)is one of the significant sources of uncertainty in building performance simulation.While OB modeling has received increased attention in the past decade,research on the degree of granularity or level of detail(LoD)required for representing occupants is still in the nascent stages.This paper analyzes the modeling and applicability of three LoDs to represent occupants in building performance assessment.A medium-sized prototype office building located in Chicago,Illinois is used as the simulation case study.Ten occupant-centric attributes are adopted to develop the LoDs for OB representation.We first demonstrate the different modeling approaches required for simulating the three fidelity levels.Later,we illustrate the suitability of the developed LoDs in supporting six building performance use cases across different lifecycle stages.This study intends to provide guidance for the building simulation community on appropriate OB representation to support various use cases.展开更多
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
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
To promote energy efficiency and emission reduction, the Chinese government has invested large amounts of resources in heat-metering reform over the past decade. However, heat-metering, which can reduce energy consump...To promote energy efficiency and emission reduction, the Chinese government has invested large amounts of resources in heat-metering reform over the past decade. However, heat-metering, which can reduce energy consumption by 15% at least in developed countries, is still not well known in China. This paper quantitatively analyzed the arousal effect of heat-metering policy on occupancy behavior regarding energy saving utilizing statistics method based on measured data of heating energy consumption of approximately 20000 users from 2008 to 2012 in Tianjin. The statistical data showed significant difference on energy consumption between users based on metering and based on area. The energy-saving rate in the heating season increased significantly from 4.11% in 2008-2009 to 10.27% in 2011-2012 as the implement of the heat-metering policy. The difference in energy-saving according to various outdoor temperatures showed that the energy-saving of occupancy behavior was more significant in a warm season than in a cold season. It also showed that the impact of heat-metering policy would be more pronounced in generally insulated buildings （15.55%） than in better insulated units （6.45%）. Besides, this paper proposed some feasible suggestions for the formulation and implementation of heat-metering policy in northern heating areas of China.展开更多
Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy con-sumption and building performance.Modeling frameworks are usually built to accomplish a certain task,but the ...Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy con-sumption and building performance.Modeling frameworks are usually built to accomplish a certain task,but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment.For complex and dynamic environments,the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable.It is expected that they will make a substantial contribution to reducing global energy consumption.Among these control techniques,the reinforcement learning(RL)method seems distinctive and applicable.The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control.Fruitful algorithms complement each other and guarantee the quality of the opti-mization.However,the examination of occupant behavior based on reinforcement learning methodologies is not well established.The way that occupant interacts with the RL agent is still unclear.This study briefly reviews the empirical applications using reinforcement learning,how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.展开更多
Manually operated solar shades have a significant impact on indoor visual comfort.This research investigates occupants’appropriate seating position and view direction in a west-facing office cell using a previously d...Manually operated solar shades have a significant impact on indoor visual comfort.This research investigates occupants’appropriate seating position and view direction in a west-facing office cell using a previously developed shade behavior model.The non-dominant sorting genetic algorithm(NSGA-II)based Multi-objective optimization was adopted to identify the optimal and near optimal solutions.Daylight and glare index were used as two visual comfort objectives for optimization and robustness of optimization results against shade behavior uncertainty that was analyzed using statistical analysis.Results show that near optimal solutions can be used instead of the optimal one since they provide more flexibility in seating positions while maintaining almost the same visual comfort performance.And thus,the appropriate seating position considering occupants’preference is 1.5m away from the external window with two view directions near parallel to the window for west-facing office rooms.展开更多
Thermal comfort of occupants is key feedback information for improving indoor environment and managing building energy use.Through analyzing inertial measurement units(IMU)data from wearable devices with ma-chine lear...Thermal comfort of occupants is key feedback information for improving indoor environment and managing building energy use.Through analyzing inertial measurement units(IMU)data from wearable devices with ma-chine learning,thermal comfort of occupants can be detected in a non-intrusive method.This paper proposed a dataset consisted of IMU data collected from 30 participants(14 males and 16 females,aged 23.23±1.70 years,height 168.67±8.02 cm,and weight 59.55±10.96 kg)who wore two IMUs on their hands while performing 30 thermal comfort activities(10 cold-related,10 hot-related,and 10 neutral activities)according to their personal habits.The database is divided into two parts:(1)Single activities data,which includes 4500 samples acquired from experiments where each participant was asked to perform 30 thermal comfort activities individually.(2)Continuous multi-activity data,which comprise 360 samples collected while participants performed a series of randomly assigned activities in a more natural and continuous manner.The combination of these two parts provides a comprehensive dataset for both the training and testing phases of machine learning models.By offering detailed labels,this database aims to serve as a foundation for research exploring machine learning applications in detecting occupant thermal comfort,ultimately contributing to improved indoor environments and more efficient building energy management.展开更多
The lighting system accounts for 8%of the total electricity consumption in commercial buildings in the United States and 12%of the total electricity consumption in public buildings globally.This consumption share can ...The lighting system accounts for 8%of the total electricity consumption in commercial buildings in the United States and 12%of the total electricity consumption in public buildings globally.This consumption share can be effectively reduced using the demand-response control.The traditional lighting system control method commonly depends on the real-time occupancy data collected using the passive infrared(PIR)sensor.However,the detection inaccuracy of the PIR sensor usually results in false-offs.To diminish the false-error frequency,the existing lighting system control simply deploys a delayed reaction period(e.g.,5 to 20 min),which is not sufficiently accurate for the demand-response operation.Therefore,in this research,a novel data-driven model predictive control(MPC)method that is based on the temporal sequential-based artificial neural network(TS-ANN)is proposed to overcome this challenge using an updated historical occupancy status.Using an office as case study,the proposed model is also compared with the traditional lighting system control method.In the proposed model,the occupancy data was trained to predict the occupancy pattern to improve the control.It was found that the occupancy prediction mainly correlates with the historical occupancy ratio and the time sequential feature.The simulation results indicated that the proposed method achieved higher accuracy(97.4%)and fewer false-offs(from 79.5 with traditional time delay method to 0.6 times per day)are achieved by the MPC model.The proposed TS-ANN-MPC method integrates the analysis of the occupant behavior routine into on-site control and has the potential to further enhance the control performance practice for maximum energy conservation.展开更多
The personal comfort system(PCS)aims to meet individual thermal comfort demands efficiently to achieve higher thermal comfort satisfaction while reducing air conditioning energy consumption.To date,many PCS devices ha...The personal comfort system(PCS)aims to meet individual thermal comfort demands efficiently to achieve higher thermal comfort satisfaction while reducing air conditioning energy consumption.To date,many PCS devices have been developed and evaluated from the perspective of thermal comfort.It will be useful for future PCS development if an approach to quantify the thermal comfort and energy performance of certain PCS devices and their combinations with consideration of user behaviors can be established.This study attempted to fill this gap by integrating thermal comfort experiments,occupancy simulations,usage behavior modeling,and building energy simulation technologies.First,human subject experiments were conducted to quantify the thermal comfort effects of the PCS.Then,the Markov chain model and conditional probability model were employed to describe the room occupancy and PCS usage behaviors.Finally,the extended comfort temperature range and user behavior models were imported into the building energy simulation tool to analyze the energy-saving potential of the PCS.The results show that the use of PCS can significantly improve occupants,thermal comfort and satisfaction rate under both warm and cool conditions.Using a cooling cushion and desktop fan can lift the upper limit of the comfortable temperature to 29.5℃while the heated cushion can extend the lower limit to 15℃.By increasing the air conditioning temperature setpoint by 2℃in summer and reducing by 2.5℃the heating temperature setpoint in winter,PCS devices can reduce heating and air conditioning energy consumption by 25%-40%while maintaining occupants’thermal comfort.展开更多
Given the importance of recognizing indoor occupant’s location and activity intensity to the control of intelligent homes,exploring approaches that can detect occupants’location and activity types while protecting o...Given the importance of recognizing indoor occupant’s location and activity intensity to the control of intelligent homes,exploring approaches that can detect occupants’location and activity types while protecting occupants’privacy is helpful.In this study,occupants’zonal location and activity intensity recognition models were developed using passive infrared(PIR)sensors and machine learning algorithms.A PIR sensor array with 15 nodes was employed to monitor indoor occupant’s and cat’s behavior in a case residential building for 71 days.The output signals of PIR sensors varied with different locations and activity intensities.By analyzing the PIR data feature,models were established using six machine learning algorithms and two sets of data.After comparing model performance,the support vector machine(SVM)algorithm was selected to establish the final models.The model input was optimized by accumulating the PIR data.Taking PIR original counting values in 1-minute and 30-minute accumulated data as input features,the optimized SVM model can achieve 99.7%accuracy under the 10-fold cross-validation for the training data set,and 90.9%accuracy for the test data set.The cat’s activity intensity is much weaker than that of occupant,yielding much smaller PIR output,which helped the SVM model to distinguish cat’s activity from occupant’s with>90%accuracy.The model’s recognition accuracy decreases with the decrease of sensor numbers and nine sensors were necessary.The findings obtained in this study support the promising future of applying PIR sensors in smart homes.展开更多
In the United States,the buildings sector consumes about 76%of electricity use and 40% of all primary energy use and associated greenhouse gas emissions.Occupant behavior has drawn increasing research interests due to...In the United States,the buildings sector consumes about 76%of electricity use and 40% of all primary energy use and associated greenhouse gas emissions.Occupant behavior has drawn increasing research interests due to its impacts on the building energy consumption.However,occupant behavior study at urban scale remains a challenge,and very limited studies have been conducted.As an effort to couple big data analysis with human mobility modeling,this study has explored urban scale human mobility utilizing three months Global Positioning System(GPS)data of 93,o00 users at Phoenix Metropolitan Area.This research extracted stay points from raw data,and identified users'home,work,and other locations by Density-Based Spatial Clustering algorithm.Then,daily mobility patterns were constructed using different types of locations.We propose a novel approach to predict urban scale daily human mobility patterns with 12-hour prediction horizon,using Long Short-Term Memory(LSTM)neural network model.Results shows the developed models achieved around 85%average accuracy and about 86%mean precision.The developed models can be further applied to analyze urban scale occupant behavior,building energy demand and flexibility,and contributed to urban planning.展开更多
This paper contributes an inclusive review of scientific studies in the field of sustainable human building ecosystems （SHBEs）. Reducing energy consumption by making buildings more energy efficient has been touted a...This paper contributes an inclusive review of scientific studies in the field of sustainable human building ecosystems （SHBEs）. Reducing energy consumption by making buildings more energy efficient has been touted as an easily attainable approach to promoting carbon-neutral energy societies. Yet, despite significant progress in research and technology development, for new buildings, as energy codes are getting more stringent, more and more technologies, e.g., LED lighting, VRF systems, smart plugs, occupancy-based controls, are used. Nevertheless, the adoption of energy efficient measures in buildings is still limited in the larger context of the developing countries and middle income/low-income population. The objective of Sustainable Human Building Ecosystem Research Coordination Network （SHBE-RCN） is to expand synergistic investigative podium in order to subdue barriers in engineering, architectural design, social and economic perspectives that hinder wider application, adoption and subsequent performance of sustainable building solutions by recognizing the essential role of human behaviors within building-scale ecosystems. Expected long-term outcomes of SHBE-RCN are collaborative ideas for transformative technologies, designs and methods of adoption for future design, construction and operation of sustainable buildings.展开更多
基金support from the National Natural Science Foundation of China under the grant No.50771095.
文摘By studying a cluster model containing Ni region （phase）, NiaAI region （phase） and Ni/Ni3Al region （interface） with a first-principles method, the occupation behavior and the ductility effect of zirconium in a Ni-Ni3Al system were investigated. It is found that zirconium has a stronger segregation tendency to Ni region than to Ni3Al region. The bond order analyses based on Rice-Wang model and the maximum theoretical shear stress model, however, show that zirconium has different degrees of ductility effect in these three regions, which originates from its different ability to increase the Griffith work of interracial cleavage 2γint and to decrease the maximum theoretical shear stress τmax. In addition, it is revealed in this paper that the distinct behavior of zirconium from boron to restrain hydrogen-induced embrittlement can be attributed to their different influences on the crystalline and electronic structures in Ni-Ni3Al alloys.
基金the financial support from the National Natural Science Foundation of China(Nos.51001001and 90922008)
文摘The influence of lattice misfit on the occupation behavior and the ductility effect of Zr in Ni-Ni3Al alloys were explored. It is found in energy analysis that the preferable site of Zr between Ni sublattice and Al sublattice will change under different lattice misfit, however, the Zr prefers to segregate Ni phase rather than Ni3Al phase in all lattice misfit range, which makes it impossible for Zr to go into Ni3Al phase to occupy Al sublattice in Ni-Ni3Al system. Bond order （BO） analysis shows that the localized ductility effect of Zr differs in different region, and the comparison between Zr-free and Zr-doped BO analysis successfully explain the mechanism of the embrittlement of Ni-Ni3Al alloys and the ductility effect of Zr.
文摘This work explores three patterns of occupants’ control of window blinds and the potential influence on daylight performance of an office room in a tropical climate. In this climate, windows are frequently obstructed by curtains to avoid glare, despite the daylighting and the exterior view. The consequences are obstructed outside view, poor daylight quality and dependency on artificial lighting. This paper assesses the impact on available daylight using parametric analysis based on daylighting dynamic computer simulations using Grasshopper and Daysim software, combining WWR （window-to-wall ratio） （40% and 80%）, SVF （sky view factor） （small and large） and occupant behavior （active, intermediate and passive users）. The user patterns are based in an office buildings survey that identifies preferences concerning daylight use and control of shading devices. The daylight performance criteria combine UDI （useful daylight illuminance） （500-5,000 lux） and illuminance uniformity distribution. Results confirm the impact of occupant behavior on daylighting performance. The optimum combination of external shading devices, high SVF and high window size results in a useful daylighting for 1/3 of the time for passive users and 2/3 for active users.
基金supported by the Michigan State University Environmental Science and Policy Program(ESPP)and Residential and Hospitality Services(RHS)In addition,statistical consultation was provided by the Center of Statistical Training and Consulting(CSTAT)at Michigan State University.
文摘Existing studies show that occupants’behavior contribute to fluctuations in energy consumption of residential units within the same building configuration.Window blinds are one of the interior design elements that the occupants use to control indoor environmental conditions.The way that occupants adjust their blinds could affect the energy performance of buildings.Thus,the purpose of this research was to identify spatial and temporal explanatory variables that correlate with occupants’use of the blinds and determine whether those variables relate to building design and surround-ing sites.Data were collected by observing how occupants in apartment buildings located in a multifamily residential complex adjust their blinds.Descriptive statistics were used to define the effect of floor level,window orientation,day of observation,the hour of observation,and weather conditions on the blind status.In addition,a generalized linear mixed model was used to predict the effect of floor level and window orientation on the occupants’adjustment of blinds.The results revealed that occupants’use of the blinds correlated significantly with spatial factors,such as the apartment buildings’floor level and windows’orientation.Interesting blind use patterns were related to temporal factors,such as the day and hour of observation.
文摘Building Performance Simulation(BPS)has been adopted as an essential tool for designing,operating,and retrofitting buildings to optimize energy efficiency throughout the building life cycle.The Green Building XML(gbXML)schema facilitates seamless data exchange between Building Information Modeling(BIM)and Building Energy Modeling(BEM)software tools.However,limited occupant behavior(OB)representation in BIM often leads to inconsistent and inaccurate energy simulation in BEM software.This paper presents 154 systematic enhancements to the existing occupant behavior XML(obXML)schema v1.3.4,initially developed for standardizing OB representation for BEM,to address existing limitations and improve interoperability with BIM models.The enhancements encompass improved integration with BIM models through extended building representations and system operations,expanded support for advanced OB models with additional environmental parameters and mathematical capabilities,and implementation of a standardized model documentation framework.To facilitate seamless data transformation between gbXML and obXML schemas,we developed a publicly available gb-obXML Schema Converter.Three case studies demonstrate the enhanced schema’s capabilities:representation of building information using a two-story office building model,documentation of a window operation behavior model,and validation of the schema converter’s functionality.The enhanced obXML schema v1.4 enables sophisticated modeling of occupant-building interactions while maintaining consistency with industry-standard BIM schemas.The standardized documentation framework facilitates reproducibility and knowledge sharing in the OB research community,while the schema converter automates the integration of building information into OB simulation workflows.These enhancements establish a foundation for more accurate building performance simulation by supporting sophisticated representation of occupant behavior within the BIM-to-BEM simulation workflows.
基金The work presented in this paper is financially supported by a strategic development special project of The Hong Kong Polytechnic University.
文摘Occupant behavior is an important factor affecting building energy consumption.Many studies have been conducted recently to model occupant behavior and analyze its impact on building energy use.However,to achieve a reduction of energy consumption in buildings,the coordination between occupant behavior and energy-efficient technologies are essential to be considered simultaneously rather than separately considering the development of technologies and the analysis of occupant behavior.It is important to utilize energy-efficient technologies to guide the occupants to avoid unnecessary energy uses.This study,therefore,proposes a new concept,“technology-guided occupant behavior”to coordinate occupant behavior with energy-efficient technologies for building energy controls.The occupants are involved into the control loop of central air-conditioning systems by actively responding to their cooling needs.On-site tests are conducted in a Hong Kong campus building to analyze the performance of“technology-guided occupant behavior”on building energy use.According to the measured data,the occupant behavior guided by the technology could achieve“cooling on demand”principle and hence reduce the energy consumption of central air-conditioning system in the test building about 23.5%,which accounts for about 7.8%of total building electricity use.
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.
文摘Simulation is recognized as an effective tool for building energy performance assessment during design or retrofit processes. Nevertheless, simulation models yield deviating outcomes from the actual building performance and a significant portion of this deviation originates from the dynamic nature of occupant behavior. Literature on occupant behavior indicates that occupant behavior is not integrated into building energy performance assessment procedures with appropriate resolution, instead they are accepted as assumed and fixed data sets that usually represent the presence of occupants. This study attempts to evaluate the effect of diverse patterns of occupant behavior on energy performance simulation for office buildings. Diverse levels of sensitivity of occupant behavior on control-based activities such as using lighting apparatus, adjusting thermostat settings, and presence in space are employed through three diverse occupant behavior patterns. These occupancy patterns are correlated with three identical office spaces simulated within a conceptual office building. EDSL Tas is used to run building energy performance simulations. Effects of occupant behavior patterns on simulation outcomes are compared for five sample winter and summer workdays, with respect to heating and cooling loads. Results present findings on how diversity of occupancy profiles influences the consumption outcomes.
基金supported by the Assistant Secretary for Energy Efficiency and Renewable Energy,Office of Building Technologies of the United States Department of Energy,under Contract No.DE-AC02-05CH11231.
文摘Occupant behavior(OB)is one of the significant sources of uncertainty in building performance simulation.While OB modeling has received increased attention in the past decade,research on the degree of granularity or level of detail(LoD)required for representing occupants is still in the nascent stages.This paper analyzes the modeling and applicability of three LoDs to represent occupants in building performance assessment.A medium-sized prototype office building located in Chicago,Illinois is used as the simulation case study.Ten occupant-centric attributes are adopted to develop the LoDs for OB representation.We first demonstrate the different modeling approaches required for simulating the three fidelity levels.Later,we illustrate the suitability of the developed LoDs in supporting six building performance use cases across different lifecycle stages.This study intends to provide guidance for the building simulation community on appropriate OB representation to support various use cases.
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.
文摘To promote energy efficiency and emission reduction, the Chinese government has invested large amounts of resources in heat-metering reform over the past decade. However, heat-metering, which can reduce energy consumption by 15% at least in developed countries, is still not well known in China. This paper quantitatively analyzed the arousal effect of heat-metering policy on occupancy behavior regarding energy saving utilizing statistics method based on measured data of heating energy consumption of approximately 20000 users from 2008 to 2012 in Tianjin. The statistical data showed significant difference on energy consumption between users based on metering and based on area. The energy-saving rate in the heating season increased significantly from 4.11% in 2008-2009 to 10.27% in 2011-2012 as the implement of the heat-metering policy. The difference in energy-saving according to various outdoor temperatures showed that the energy-saving of occupancy behavior was more significant in a warm season than in a cold season. It also showed that the impact of heat-metering policy would be more pronounced in generally insulated buildings （15.55%） than in better insulated units （6.45%）. Besides, this paper proposed some feasible suggestions for the formulation and implementation of heat-metering policy in northern heating areas of China.
基金The authors are thankful for the financial support from IMMA project of research network(391836)Dalarna University,Sweden and Inter-national science and technology cooperation center in Hebei Province(20594501D),China.
文摘Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy con-sumption and building performance.Modeling frameworks are usually built to accomplish a certain task,but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment.For complex and dynamic environments,the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable.It is expected that they will make a substantial contribution to reducing global energy consumption.Among these control techniques,the reinforcement learning(RL)method seems distinctive and applicable.The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control.Fruitful algorithms complement each other and guarantee the quality of the opti-mization.However,the examination of occupant behavior based on reinforcement learning methodologies is not well established.The way that occupant interacts with the RL agent is still unclear.This study briefly reviews the empirical applications using reinforcement learning,how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.
基金supported by the National Natural Science Foundation of China under Grant No.51878358Natural Science Foundation of Zhejiang Province under Grant No.LY18E080012National Key Technology R&D Program of the Ministry of Science and Technology under Grant 2013BAJ10B06.
文摘Manually operated solar shades have a significant impact on indoor visual comfort.This research investigates occupants’appropriate seating position and view direction in a west-facing office cell using a previously developed shade behavior model.The non-dominant sorting genetic algorithm(NSGA-II)based Multi-objective optimization was adopted to identify the optimal and near optimal solutions.Daylight and glare index were used as two visual comfort objectives for optimization and robustness of optimization results against shade behavior uncertainty that was analyzed using statistical analysis.Results show that near optimal solutions can be used instead of the optimal one since they provide more flexibility in seating positions while maintaining almost the same visual comfort performance.And thus,the appropriate seating position considering occupants’preference is 1.5m away from the external window with two view directions near parallel to the window for west-facing office rooms.
基金the support of this research by the National Natural Science Foundation of China(No.52278117)the Philosophical and Social Science Program of Guangdong Province,China(GD22XGL20)the Shenzhen Science and Technology Pro-gram(Nos.20220531101800001 and 20220810160221001).
文摘Thermal comfort of occupants is key feedback information for improving indoor environment and managing building energy use.Through analyzing inertial measurement units(IMU)data from wearable devices with ma-chine learning,thermal comfort of occupants can be detected in a non-intrusive method.This paper proposed a dataset consisted of IMU data collected from 30 participants(14 males and 16 females,aged 23.23±1.70 years,height 168.67±8.02 cm,and weight 59.55±10.96 kg)who wore two IMUs on their hands while performing 30 thermal comfort activities(10 cold-related,10 hot-related,and 10 neutral activities)according to their personal habits.The database is divided into two parts:(1)Single activities data,which includes 4500 samples acquired from experiments where each participant was asked to perform 30 thermal comfort activities individually.(2)Continuous multi-activity data,which comprise 360 samples collected while participants performed a series of randomly assigned activities in a more natural and continuous manner.The combination of these two parts provides a comprehensive dataset for both the training and testing phases of machine learning models.By offering detailed labels,this database aims to serve as a foundation for research exploring machine learning applications in detecting occupant thermal comfort,ultimately contributing to improved indoor environments and more efficient building energy management.
基金This study was supported by the National Natural Science Foundation of China(No.51778321):Research on the quantitative description and simulation methodology of occupant behavior in buildingsthe Innovative Research Groups of the National Natural Science Foundation of China(No.51521005)also the Tsinghua University tutor research fund.
文摘The lighting system accounts for 8%of the total electricity consumption in commercial buildings in the United States and 12%of the total electricity consumption in public buildings globally.This consumption share can be effectively reduced using the demand-response control.The traditional lighting system control method commonly depends on the real-time occupancy data collected using the passive infrared(PIR)sensor.However,the detection inaccuracy of the PIR sensor usually results in false-offs.To diminish the false-error frequency,the existing lighting system control simply deploys a delayed reaction period(e.g.,5 to 20 min),which is not sufficiently accurate for the demand-response operation.Therefore,in this research,a novel data-driven model predictive control(MPC)method that is based on the temporal sequential-based artificial neural network(TS-ANN)is proposed to overcome this challenge using an updated historical occupancy status.Using an office as case study,the proposed model is also compared with the traditional lighting system control method.In the proposed model,the occupancy data was trained to predict the occupancy pattern to improve the control.It was found that the occupancy prediction mainly correlates with the historical occupancy ratio and the time sequential feature.The simulation results indicated that the proposed method achieved higher accuracy(97.4%)and fewer false-offs(from 79.5 with traditional time delay method to 0.6 times per day)are achieved by the MPC model.The proposed TS-ANN-MPC method integrates the analysis of the occupant behavior routine into on-site control and has the potential to further enhance the control performance practice for maximum energy conservation.
基金supported by the National Natural Science Foundation of China(No.51908414,No.52108086)China National Key R&D Program during the 13th Five-year Plan Period(No.2017YFC0702200).
文摘The personal comfort system(PCS)aims to meet individual thermal comfort demands efficiently to achieve higher thermal comfort satisfaction while reducing air conditioning energy consumption.To date,many PCS devices have been developed and evaluated from the perspective of thermal comfort.It will be useful for future PCS development if an approach to quantify the thermal comfort and energy performance of certain PCS devices and their combinations with consideration of user behaviors can be established.This study attempted to fill this gap by integrating thermal comfort experiments,occupancy simulations,usage behavior modeling,and building energy simulation technologies.First,human subject experiments were conducted to quantify the thermal comfort effects of the PCS.Then,the Markov chain model and conditional probability model were employed to describe the room occupancy and PCS usage behaviors.Finally,the extended comfort temperature range and user behavior models were imported into the building energy simulation tool to analyze the energy-saving potential of the PCS.The results show that the use of PCS can significantly improve occupants,thermal comfort and satisfaction rate under both warm and cool conditions.Using a cooling cushion and desktop fan can lift the upper limit of the comfortable temperature to 29.5℃while the heated cushion can extend the lower limit to 15℃.By increasing the air conditioning temperature setpoint by 2℃in summer and reducing by 2.5℃the heating temperature setpoint in winter,PCS devices can reduce heating and air conditioning energy consumption by 25%-40%while maintaining occupants’thermal comfort.
基金supported by the National Natural Science Foundation of China(No.51908414)China National Key R&D Program during the 13th Five-year Plan Period(No.2017YFC0702200)。
文摘Given the importance of recognizing indoor occupant’s location and activity intensity to the control of intelligent homes,exploring approaches that can detect occupants’location and activity types while protecting occupants’privacy is helpful.In this study,occupants’zonal location and activity intensity recognition models were developed using passive infrared(PIR)sensors and machine learning algorithms.A PIR sensor array with 15 nodes was employed to monitor indoor occupant’s and cat’s behavior in a case residential building for 71 days.The output signals of PIR sensors varied with different locations and activity intensities.By analyzing the PIR data feature,models were established using six machine learning algorithms and two sets of data.After comparing model performance,the support vector machine(SVM)algorithm was selected to establish the final models.The model input was optimized by accumulating the PIR data.Taking PIR original counting values in 1-minute and 30-minute accumulated data as input features,the optimized SVM model can achieve 99.7%accuracy under the 10-fold cross-validation for the training data set,and 90.9%accuracy for the test data set.The cat’s activity intensity is much weaker than that of occupant,yielding much smaller PIR output,which helped the SVM model to distinguish cat’s activity from occupant’s with>90%accuracy.The model’s recognition accuracy decreases with the decrease of sensor numbers and nine sensors were necessary.The findings obtained in this study support the promising future of applying PIR sensors in smart homes.
基金supported by the U.S.National Science Foundation(Award No.1949372 and No.2125775)in part supported through computational resources provided by Syracuse University.
文摘In the United States,the buildings sector consumes about 76%of electricity use and 40% of all primary energy use and associated greenhouse gas emissions.Occupant behavior has drawn increasing research interests due to its impacts on the building energy consumption.However,occupant behavior study at urban scale remains a challenge,and very limited studies have been conducted.As an effort to couple big data analysis with human mobility modeling,this study has explored urban scale human mobility utilizing three months Global Positioning System(GPS)data of 93,o00 users at Phoenix Metropolitan Area.This research extracted stay points from raw data,and identified users'home,work,and other locations by Density-Based Spatial Clustering algorithm.Then,daily mobility patterns were constructed using different types of locations.We propose a novel approach to predict urban scale daily human mobility patterns with 12-hour prediction horizon,using Long Short-Term Memory(LSTM)neural network model.Results shows the developed models achieved around 85%average accuracy and about 86%mean precision.The developed models can be further applied to analyze urban scale occupant behavior,building energy demand and flexibility,and contributed to urban planning.
基金The support through a grant from US National Science Foundation （Award# 1338851） is greatly appreciated. The SHBERCN activities enjoy the broad supports from IEA Annex 66 group, US DOE＇s Building Technology Office, and Lawrence Berkeley National Laboratories.
文摘This paper contributes an inclusive review of scientific studies in the field of sustainable human building ecosystems （SHBEs）. Reducing energy consumption by making buildings more energy efficient has been touted as an easily attainable approach to promoting carbon-neutral energy societies. Yet, despite significant progress in research and technology development, for new buildings, as energy codes are getting more stringent, more and more technologies, e.g., LED lighting, VRF systems, smart plugs, occupancy-based controls, are used. Nevertheless, the adoption of energy efficient measures in buildings is still limited in the larger context of the developing countries and middle income/low-income population. The objective of Sustainable Human Building Ecosystem Research Coordination Network （SHBE-RCN） is to expand synergistic investigative podium in order to subdue barriers in engineering, architectural design, social and economic perspectives that hinder wider application, adoption and subsequent performance of sustainable building solutions by recognizing the essential role of human behaviors within building-scale ecosystems. Expected long-term outcomes of SHBE-RCN are collaborative ideas for transformative technologies, designs and methods of adoption for future design, construction and operation of sustainable buildings.
