The First-principles Study on the Occupation Behavior and the Ductility Mechanism of Zr in Ni-Ni_3Al System with Lattice Misfit

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.

First-principles Study on the Ductility Effect of Zirconium and Its Distinct Behavior from Boron to Restrain Hydrogen-induced Embrittlement in Ni-Ni3Al Alloys

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.

The Impact of Occupant Behavior on Daylight Performance of an Office Room in a Tropical Climate

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.

Energy consumption simulations of rural residential buildings considering differences in energy use behavior among family members

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.

Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior

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.

On the applicability of various levels of detail for occupant behavior representation and modeling in building performance simulation

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.

Modelling method for air-conditioning usage behavior in multi-occupant office space based on group decision-making strategy

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.

Modeling urban scale human mobility through big data analysis and machinelearning

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.

Investigation on occupant presence and appliance operation schedules for university campus in south China sub-tropical area

Building occupant presence during varying periods is crucial to the performance studies of buildings and city regions.However,the understanding of the building occupancies on the university campus remains limited.To address this gap,our study employs field measurements,payment records,course arrangements,and building access systems to depict the occupancy patterns of the canteen,dormitory,library,and teaching and lab buildings during weekdays and weekends.We found that the occupancy rates across different buildings are somehow interrelated,given that the total number of occupants on campus is generally constant.Notably,dormitory occupancy rates tend to be low during the morning and afternoon course hours,which inversely correlates with the high occupancy rates in the teaching and lab buildings during these periods.Similarly,canteens experience surges in occupancy during meal times,which coincide with a decrease in library usage.Moreover,we established appliance operation schedules for dormitories through surveys and on-site investigations.Water dispensers and electronic devices were identified as the primary energy consumers for both male and female occupants,with desk-top fans and hairdryers being significant energy users for male and female occupants,respectively.These findings are essential for energy studies within a campus setting,underlining the importance of considering occupant behaviors on a regional scale.

Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use

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.

Evaluating diverse patterns of occupant behavior regarding control-based activities in energy performance simulation

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.

Quantitative research of incentive effects of heat-metering policy on energy-saving behavior in Tianjin

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.

The reinforcement learning method for occupant behavior in building control:A review

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.

Impact of occupant autonomy on satisfaction and building energy efficiency

The philosophy of building energy management is going through a paradigm change from traditional,often inefficient,user-controlled systems to one that is centrally automated with the aid of IoT-enabled technologies.In this context,occupants’perceived control and building automation may seem to be in conflict.The inquiry of this study is rooted in a proposition that while building automation and centralized control systems are assumed to provide indoor comfort and conserve energy use,limiting occupants’control over their work environment may result in dissatisfaction,and in turn decrease productivity.For assessing this hypothesis,data from the post-occupancy evaluation survey of a smart building in a university in Australia was used to analyze the relationships between perceived control,satisfaction,and perceived productivity.Using structural equation modeling,we have found a positive direct effect of occupants’perceived control on overall satisfaction with their working area.Meanwhile,perceived control exerts an influence on perceived productivity through satisfaction.Furthermore,a field experiment conducted in the same building revealed the potential impact that occupant controllability can have on energy saving.We changed the default light settings from automatic on-and-offto manual-on and automatic-off,letting occupants choose themselves whether to switch the light on or not.Interestingly,about half of the participants usually kept the lights off,preferring daylight in their rooms.This also resulted in a reduction in lighting electricity use by 17.8%without any upfront investment and major technical modification.These findings emphasize the important role of perceived control on occupant satisfaction and productivity,as well as on the energy-saving potential of the user-in-the-loop automation of buildings.

A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development

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.

Room zonal location and activity intensity recognition model for residential occupant using passive-infrared sensors and machine learning

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.

Energy and comfort performance of occupant-centric air conditioning strategy in office buildings with personal comfort devices

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.

Energy modeling and data structure framework for Sustainable Human-Building Ecosystems （SHBE）- a review

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.

佛山市南海区职业暴露人群布鲁菌病防控知识与行为的分析

目的了解佛山市南海区职业暴露人群对布鲁菌病(布病)防治知识的知晓情况以及高危行为习惯和个人防护行为情况,分析危险因素,为制定科学防治策略提供依据。方法通过多阶段PPS分层随机抽样方法对佛山市南海区内职业暴露人群开展布病防控知识与行为的问卷调查,对调查资料进行卡方检验。结果共调查职业暴露人群154人,不同工种的性别、文化程度、工作年限差异均有统计学意义。被调查人员中布病防治知识总体知晓率不高,不同工种知晓率差异有统计学意义(χ^(2)=24.201,P<0.05);不同职业对于布病知识知晓率不全相同,其中屠宰人员布病知识知晓率最低;从事交易和皮毛乳肉加工的人员中高危行为发生率较从事屠宰的低,但都存在较大的感染风险;被调查人员普遍对布病的个人防护不足。结论应进一步做好动物检验检疫工作,为各职业暴露人群建立健康档案、定期开展布病筛查,实施针对性强且结合实际的健康教育培训和行为干预,以加强布病防控。