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The design of Activity-Based Working (ABW) environments embraces workers' continuous mobility enabled by technology and the mindset of seeking work zones that best support the task at hand. This paper focuses on aspects of workspace selection within a facility designed to support ABW, focusing on the overall occupancy dynamics, temporal context, and information capturing less-explored details of the physical environment. This study analyses the active use of a workspace in relation to work desk shapes, rectangular and trapezial. Drawing from a longitudinal dataset spanning 12 months from an ABW facility, capturing the active workstation usage of 964 occupants through individual computer logins, this study employs descriptive statistics to analyse the active use of workspace relative to total work hours over the year. Inferential statistical techniques are utilised to compare active use measurements between and within specific workspace areas, revealing significant differences and highlighting the importance of temporal and spatial contexts in workspace utilisation patterns. The presented results demonstrate both tendencies and statistically significant differences, confirming the relevance of the studied variables in examining workspace utilisation. The results show significant usage variations throughout the day across different zones of the observed workspace, with peak activity between 11:00 and 13:00 h for both work desk shapes. This study's insights are relevant to improving the utilisation of facilities designed for ABW and contribute to a longstanding interest in designing and arranging workplaces to better fit the people who use them. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is a finite resource and necessary nutrient for agriculture. Urine contains a higher concentration of P than domestic wastewater, which can be recovered by source separation and treatment (hereafter urine diversion). Commercial and institutional (CI) buildings are a logical location for urine diversion since restrooms account for a substantial fraction of water use and wastewater generation. This study estimated the potential for P recovery from human urine and water savings from reduced flushing in CI buildings, and proposed an approach to identify building types and community layouts that are amenable to implementing urine diversion. The results showed that urine diversion is most advantageous in CI buildings with either high daily occupancy counts or times, such as hospitals, schools, office buildings, and airports. Per occupant P recovery benchmarks were estimated to be between 0.04–0.68 g/cap·d. Per building P recovery rates were estimated to be between 0.002–5.1 kg/d, and per building water savings were estimated to be between 3 and 23 % by volume. Recovered P in the form of phosphate fertilizer and potable water savings could accrue profits and cost reductions that could offset the capital costs of new urine diversion systems within 5 y of operation. Finally, urine diversion systems can be implemented at different levels of decentralization based on community layout and organizational structure, which will require socioeconomic and policy acceptance for wider adoption.

The design of Activity-Based Working (ABW) environments embraces workers’ continuous mobility enabled by technology and the mindset of seeking work zones that best support the task at hand. This paper focuses on aspects of workspace selection within a facility designed to support ABW, focusing on the overall occupancy dynamics, temporal context, and information capturing less-explored details of the physical environment. This study analyses the active use of a workspace in relation to work desk shapes, rectangular and trapezial. Drawing from a longitudinal dataset spanning 12 months from an ABW facility, capturing the active workstation usage of 964 occupants through individual computer logins, this study employs descriptive statistics to analyse the active use of workspace relative to total work hours over the year. Inferential statistical techniques are utilised to compare active use measurements between and within specific workspace areas, revealing significant differences and highlighting the importance of temporal and spatial contexts in workspace utilisation patterns. The presented results demonstrate both tendencies and statistically significant differences, confirming the relevance of the studied variables in examining workspace utilisation. The results show significant usage variations throughout the day across different zones of the observed workspace, with peak activity between 11:00 and 13:00 h for both work desk shapes. This study’s insights are relevant to improving the utilisation of facilities designed for ABW and contribute to a longstanding interest in designing and arranging workplaces to better fit the people who use them.

Building occupancy research increasingly emphasizes understanding the social and physical dynamics of how people occupy space. Opportunities in the open source domain including social media, Volunteered Geographic Information, crowdsourcing, and sensor data have proliferated, resulting in the exploration of building occupancy dynamics at varying spatiotemporal scales. At Oak Ridge National Laboratory, research into building occupancies through the development of a global learning framework that accommodates exploitation of open source authoritative sources, including governmental census and surveys, journal articles, real estate databases, and more, to report national and subnational building occupancies across the world continues through the Population Density Tables (PDT) project. This probabilistic learning system accommodates expert knowledge, experience, and open-source data to capture local, socioeconomic, and cultural information about human activity. It does so through a systematic process of data harmonization techniques in the development of observation models for over 50 building types to dynamically update baseline estimates and report probabilistic diurnal and episodic building occupancy estimates. This discussion will explore how PDT is implemented at scale and expanded based on the development of observation model classes and will explain how to interpret and spatially apply the reported probability occupancy estimates and uncertainty. [ABSTRACT FROM AUTHOR]

With the rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most viable platforms for improving building design and management. Researchers investigated the possibility of integrating energy management systems with cyber-physical systems to form energy-cyber-physical systems in order to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is a major source of uncertainty for energy management, ignoring it often results in both energy waste caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposes an occupancy-linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilizes ensemble classification algorithms to extract three forms of occupancy information. It creates a data interface to link energy management systems and cyber-physical systems and allows for automated occupancy detection and interpretation by assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy-linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy data type, the proposed model can potentially save about 26.4% of energy consumption in cooling and ventilation demands.

With the rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most viable platforms for improving building design and management. Researchers investigated the possibility of integrating energy management systems with cyber-physical systems to form energy-cyber-physical systems in order to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is a major source of uncertainty for energy management, ignoring it often results in both energy waste caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposes an occupancy-linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilizes ensemble classification algorithms to extract three forms of occupancy information. It creates a data interface to link energy management systems and cyber-physical systems and allows for automated occupancy detection and interpretation by assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy-linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy data type, the proposed model can potentially save about 26.4% of energy consumption in cooling and ventilation demands.

The energy demand has increased worldwide, and the construction industry makes up a high percentage of energy consumption. Different design components, construction, and exploitation regarding construction energy consumption and the drive towards sustainability have been considered; however, energy conservation with an emphasis on the user's behaviours has been ignored. This research aims to provide a quantitative definition of the impact of behaviour on energy Loads in three residential, institutional, and educational occupancies in one apartment through survey and simulation. In this research, by allocating three different occupancies to one building in Qom, each occupant's cooling and heating loads have been compared in a one-year interval. First, the building modelling was carried out in Ecotet software and put in Energyplus software. Then, the outcomes were compared by assuming a single building and describing three different patterns of using the space in Energyplus. The results show that the reduction or increase in energy loads in each occupancy was influenced by the number of users and the patterns of their activities or clothing. Reducing the duration of the presence or changing the work hours in warm seasons of the year can significantly help reduce energy consumption in educational and institutional occupancies in hot and dry climates. The residential users' economic motives can be one of the reasons for reduced energy consumption in residential occupancies compared to institutional occupancies. [ABSTRACT FROM AUTHOR]

Assessing building performance related to energy consumption in post-design-occupancy stage requires knowledge of building occupancy pattern. These occupancy data can potentially be collected from trials and used to improve the prediction capability of building performance models. Due to the limitations of passive sensors in detecting an individual's occupancy throughout the building, indoor positioning can provide a viable alternative. Previous work on using indoor positioning techniques for detecting building occupancy mainly focused on passive monitoring through Wi-Fi or BLE proximity sensing by estimating the number of occupants at any given time. This paper extends our previous research and demonstrates the merit of occupancy monitoring through active tracking at an individual level using a smartphone-based multi-floor indoor positioning system. The paper discusses the design of a novel occupancy detection trial setup, mimicking real-world office occupancy and discusses the outcome of the ecologically valid trials using the developed positioning system. In total 50 occupancy trials were carried out by around 22 participants comprising of a variety of routes within the building. The trial results are presented to demonstrate the level of accuracy achievable against a specific set of the performance metric necessary for building occupancy detection and modelling. [ABSTRACT FROM AUTHOR]

University buildings are one of the most relevant closed environments in which the COVID-19 event clearly pointed out stakeholders' needs toward safety issues, especially because of the possibility of day-to-day presences of the same users (i.e. students, teachers) and overcrowding causing long-lasting contacts with possible "infectors". While waiting for the vaccine, as for other public buildings, policy-makers' measures to limit virus outbreaks combine individual's strategies (facial masks), occupants' capacity and access control. But, up to now, no easy-to-apply tools are available for assessing the punctual effectiveness of such measures. To fill this gap, this work proposes a quick and probabilistic simulation model based on consolidated proximity and exposure-time-based rules for virus transmission confirmed by international health organizations. The building occupancy is defined according to university scheduling, identifying the main "attraction areas" in the building (classrooms, break-areas). Scenarios are defined in terms of occupants' densities and the above-mentioned mitigation strategies. The model is calibrated on experimental data and applied to a relevant university building. Results demonstrate the model capabilities. In particular, it underlines that if such strategies are not combined, the virus spreading can be limited by only using high protection respiratory devices (i.e. FFP3) by almost every occupant. On the contrary, the combination between access control and building capacity limitation can lead to the adoption of lighter protective devices (i.e. surgical masks), thus improving the feasibility, users' comfort and favorable reception. Simplified rules to combine acceptable mask filters-occupants' density are thus provided to help stakeholders in organizing users' presences in the building during the pandemic. [ABSTRACT FROM AUTHOR]

The prediction of the occupancy in buildings is essential to design efficient energy control strategies that optimize consumption and reduce losses while guaranteeing the comfort of the occupants. For this reason, many works address the problem of detecting, estimating, and predicting buildings' occupancy using different techniques, devices, and technologies. The occupancy prediction process can be described in four stages: data acquisition, modeling, evaluation, and testing, which are closely related. This paper reviews the most relevant recent literature on building occupancy estimation and prediction, analyzing the key aspects of its stages. A detailed description of the variables and design considerations is presented, including measurement methods, sensor selection, modeling techniques, evaluation metrics, and different applications. Through its examination, this paper elaborates significant remarks on the interaction between the stages, providing an overview of the suitable design of the occupancy prediction process. Finally, current and future trends are discussed. • A systematic review of the occupancy estimation and prediction process is presented. • Data acquisition, modeling, evaluation, and testing are the four general stages. • The importance of sensor fusion in overcoming individual limitations is presented. • Occupancy detection methods include deterministic, stochastic, and machine learning. • Some potential future research directions are discussed. [ABSTRACT FROM AUTHOR]

Several approaches exist for simulating building properties (e.g. temperature, noise) and human occupancy (e.g. movement, actions) in an isolated fashion, providing limited ability to represent how environmental features affect human behaviour and vice versa. To systematically model building-occupant interactions, several requirements must be met, including the modelling of (a) interdependent multi-domain phenomena ranging from temperature and sound changes to human movement, (b) high-level occupant planning and low-level steering behaviours, (c) environmental and occupancy phenomena that unfold at different time scales, and (d) multiple strategies to represent occupancy using established models. In this work, we propose an integrated platform that satisfies the aforementioned requirements thus enabling the joint simulation of building-occupant interactions. To this end, we combine the benefits of a model-independent, discrete-event, general-purpose framework with an established crowd simulator. Our platform provides insights on a building's performance while accounting for alternative design features and modelling strategies. [ABSTRACT FROM AUTHOR]

Recent studies show that urban contextual form can influence building energy use intensity (EUI). While many studies have focused on the thermal properties of buildings and their environment, human behavior, a key determinant of EUI, is also influenced by the contextual form. Urban walkability, a key measure of urban vitality, can encourage outdoor activity and thus alter occupant behavior and outsource energy demand. This study examines the behavioral aspect of the context effect on residential building EUI in Seoul. Summer electricity, winter gas, annual electricity, and annual gas EUIs are individually analyzed. Two sets of analyses are executed for each energy type. First, walkability is used as a simple explanatory variable in an ordinary least squares regression model. Second, walkability is used as a moderator for the socioeconomic parameters of occupant density, occupant age, and real estate price. The results suggest that an increase in walkability can significantly reduce residential electricity EUI, and walkability can modify the influence of occupant density and occupant age on electricity EUI. However, walkability is only a marginally significant predictor of residential gas EUI, and all interactions with socioeconomic variables are insignificant. These findings highlight the importance of walkability as a means of linking urban form and building energy. • Walkability is used as a contextual predictor of building's electricity and gas use. • Walkability significantly influences building's annual and summer season electricity use. • Walkability is significant moderator of occupant density's influence on electricity use. • Walkability significantly influences building's annual gas use, but is insignificant for winter peak. [ABSTRACT FROM AUTHOR]

Predicting electricity consumption represents one of most important information for efficient energy management in smart buildings. It is mainly used for occupancy prediction and the development of optimized control approaches of building's appliances (e.g., lighting and heating/air conditioning systems). Recently, several approaches have been proposed for load profiling, prediction and forecasting. The work presented in this paper is towards the development of load forecasting approaches for being integrated for occupancy prediction and context-driven control of building's appliances. We mainly investigated the accuracy of various machine learning and statistical methods for forecasting energy consumption. An IoT and Big Data based platform was deployed for gathering near-real time data about electricity/load consumption. Recorded data were used to deploy predictive models using ARIMA, SARIMA, XGBoost, Random Forest (RF), and Long Short-Term Memory. Experiments have been conducted and results are reported to shed more light on the accuracy of these methods for load forecasting. [ABSTRACT FROM AUTHOR]

Highlights • A review of Data analytics approaches that were used to estimate building occupancy is presented. • Unique literature pertaining to occupancy detection is categorized into three main groups: Occupancy detection, counting and tracking/special applications. • A comprehensive table comparing various methods is presented as a guideline to readers. Abstract A review of the literature pertaining to building occupancy detection, counting, and tracking – three areas under the umbrella of building occupancy estimation – is presented with a focus on mathematical approaches and corresponding metrics. The idea is to provide the reader with a background on the hardware and techniques used for occupancy inference, subsequent to data collection, with emphasis placed on the algorithmic characterization of occupancy estimation. The various approaches employed by researchers to tackle the problem are surveyed and summarized, including: data collection, cleaning processes, algorithm utilization and categorization, as well as data structuring and organization. The scope of prediction and performance metrics are used to establish a benchmarking system through a comprehensive summary of (indoor) occupancy estimation, presented in the context of the mathematical tools utilized. [ABSTRACT FROM AUTHOR]

A comparative assessment of external and internal thermal insulation for an intermittently air-conditioned building was performed numerically. The energy consumed for cooling was used to evaluate the exterior wall thermal insulation configurations. This study examined the effect of the building occupancy, the air conditioner operation mode, and the lumped indoor heat gain. The results showed that the external thermal insulation configuration provides better thermal performance than the internal insulation configuration when the building was occupied during the day. When the building was occupied during the night, the internal thermal insulation configuration showed better thermal performance if the air conditioner runs continuously during occupied hours. When the air conditioner runs intermittently by applying building thermal storage, the suitable thermal insulation configuration depended on the lumped indoor heat gain. The determination of the exterior wall insulation configuration should consider three factors: the building occupancy, the air conditioner operation mode, and the lumped indoor heat gain. [ABSTRACT FROM AUTHOR]

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - Human Occupancy Counter Plus Plus (DA-HOC++), a robust way to estimate the number of people within one room by using data from a carbon dioxide sensor. In our previous work, the proposed Seasonal Decomposition for Human Occupancy Counting (SD-HOC) model can accurately predict the number of individuals when the training and labelled data are adequately available. DA-HOC++ is able to predict the number of occupants with minimal training data: as little as 1 day's data. DA-HOC++ accurately predicts indoor human occupancy for five different rooms across different countries using a model trained from a small room and adapted to other rooms. We evaluate DA-HOC++ with two baseline methods: a support vector regression technique and an SD-HOC model. The results demonstrate that DA-HOC++'s performance on average is better by 10.87% in comparison to SVR and 8.65% in comparison to SD-HOC. [ABSTRACT FROM AUTHOR]

Human occupancy information is crucial for any modern Building Management System (BMS). Implementing pervasive sensing and leveraging Carbon Dioxide data from BMS sensor, we present large Room Utilisation Prediction with carbon dioxide sensor (RUP), a novel way to estimate the number of people within a closed space from a single carbon dioxide sensor. RUP de-noises and pre-processes the carbon dioxide and indoor human occupancy data. We utilise both seasonal-trend decomposition based on Loess and seasonal-trend decomposition with moving average to factorise both datasets. For each trend, seasonal and irregular component, we model different regression algorithms to predict each respective human occupancy component value. We propose a zero pattern adjustment model to increase the accuracy and finally, we use additive decomposition to reconstruct the prediction value. We run our model in two different locations that have different contexts. The first location is an academic staff room and the second is a cinema theatre with up to 300 people. Our results show an average of 4.33% increment in accuracy for the small room with 94.68% indoor human occupancy counting and 8.46% increase for the cinema theatre in comparison to the accuracy of the baseline method, support vector regression. [ABSTRACT FROM AUTHOR]

University campus is a typical and special type of urban settings. Smart campus has been developing drastically based on the technologies and practices of smart cities (Verstaevel et al., 2017). Human movements on campus, which are combinations of regular and irregular itineraries, are mapped into digital trajectories with various positioning technologies. The human movement trajectories can further be used to optimize the design and management of smart campus. The estimated occupancy of buildings or facilities can show the hot spots and cold spots, which can help save energy and optimize space utilization. In this case, geo-visualization technique plays an important and unique role in revealing spatiotemporal patterns of human mobility and utilization of infrastructures (Oppermann and Munzner, 2020). In this work, we design and implement a procedure for generating and visualizing people’s spatial trajectories and building occupancy based on anonymised network accessing log data generated by the Wi-Fi networks on campus. [ABSTRACT FROM AUTHOR]

Buildings' occupant profiles at the urban scale play an important role in various applications like Urban Building Energy Modeling (UBEM) and assessing energy consumption patterns and distribution at the urban scale. Due to the lack of data availability and cost-consuming process, estimating the building occupancy profile at the urban scale is challenging, and fixed building occupant profiles are primarily used in previous studies, leading to errors in calculating building energy consumption. In recent years, novel data sources have created an opportunity for a deeper understanding of building occupant patterns. This paper comprehensively reviews different aspects and categories of building occupant profiles at the urban scale, approaches, and available data sources. It includes a detailed comparison between different data sources and assesses their capability for estimating the building occupant profiles at the urban scale. General strategies to estimate the building occupant profile at the urban scale and its interrelation with human mobility are portrayed in detail. Additionally, research gaps and potential future directions of the urban-scale building occupancy field of study are also explored. • Characteristics of urban-scale building occupancy are well-categorized and presented. • Potential data sources for estimating urban-scale building occupancy are investigated. • The role of human mobility datasets in urban-scale building occupancy is discussed. • Future research recommendations for urban-scale building occupancy are presented. [ABSTRACT FROM AUTHOR]

• Provided a platform to test various residential building control systems. • Used probability of occupancy to determine setback temperature when unoccupied. • Integrated optimizer with adaptive and occupancy control. • Generated dynamic electricity tariff based on wholesale electricity pricing. • Evaluated the peak load shaving potential with dynamic pricing and optimization. Energy consumption in buildings continues to rise with increased deployment of energy-consuming equipment such as Heating, Ventilation, and Air Conditioning (HVAC) amid a growing world economy. Renewable energy is projected to comprise a majority of the future electricity supply, but the intermittent nature of renewables means that consumption must respond to dynamic supply for optimal utilization. This paper proposes a novel HVAC control strategy for residential buildings using the adaptive comfort model, considering occupancy through probability and real-time information, and optimizing the HVAC schedule to reduce cost, maintain thermal comfort, and respond to the dynamic availability of renewable energy while being generalizable to different situations. To validate this approach, the Universal CPS Environment for Federation (UCEF) co-simulation platform is used to connect advanced building controls with the building energy simulation software EnergyPlus. Simulations are performed for a residential building in Sacramento, CA during a typical summer week. Economic impacts, energy consumption, and thermal comfort are analyzed for traditional, adaptive, and occupancy-based control strategies under demand-based, tiered, and fixed electric tariff systems. Simulation results show that occupancy consideration, adaptive thermal comfort, and optimization can reduce cost by 50.1 %, electricity consumption by 52.9 %, and discomfort by 56.2 % compared to traditional fixed setpoints. The ability of the proposed HVAC control strategy to shift energy consumption away from peak times under a demand-based tariff system is qualitatively analyzed and findings suggest that maximum load-shifting on a grid-scale is attained using occupancy consideration with optimized control and demand-based pricing. For individual residential buildings, similar economic benefits can be gained using the less-complex adaptive HVAC control strategy with existing tiered or simple electric tariff systems. [ABSTRACT FROM AUTHOR]

This paper presents the part of the research that has been done at the Universities both in Belgrade and Thessaloniki, Greece, taking into account indoor environmental quality in office buildings and classrooms. The measurements that are presented were done in Process Equipment Design Laboratory at Aristotle University Thessaloniki, during March 2015. Indoor environmental quality regarding air temperature, relative humidity, and CO2 concentration in two representative offices is observed. The similar offices are located one on the north-east and the other one on the south-west side of the University building, so as to be representative of the orientation's impact. Furthermore, the impact of natural ventilation on CO2 concentration and temperature is monitored, together with the offices ' occupancy. Recommended parameters for indoor air quality are compared and discussed on the base of several standards: SRPS EN 15251:2010, ASHRAE standards 55 and 62.1, and ISO 7730. The main objectives, as set from these standards are discussed, together with the investigation results. [ABSTRACT FROM AUTHOR]

The modeling of energy consumption in buildings must consider occupancy as a relevant input, since it plays a very important role in the overall building's energy consumption. Frequently, buildings lack of permanent occupancy monitoring solutions. However, they may include data sources that are correlated with real building occupancy. This study proposes a new methodology for energy consumption modeling, supported by these alternative data sources, such as the number of vehicles in a parking lot. The aim is to mitigate investment in permanent occupancy monitoring solutions. The proposed methodology makes use of short-term real occupancy monitoring for model fitting, to enable the development of occupancy and energy consumption models, based on these alternative data sources. [ABSTRACT FROM AUTHOR]

Buildings of religious worship constitute nearly 8% of all commercial buildings in the United States. However, to date, religious facilities have received inadequate attention in the literature. Religious facilities exhibit unique patterns of occupancy and energy usage that do not reflect the general patterns of office, education, and other commercial buildings. This paper characterizes building lighting consumption and occupancy patterns in religious facilities to identify potential energy-saving opportunities through a long-term, in-depth experimental study. Occupancy and lighting schedules are experimentally determined for two architecturally identical church buildings in different climates. General trends of occupancy reveal intermittent, but consistent, use of the building. The feasibility of occupancy based lighting control in these buildings is evaluated. Experimental results of occupancy based lighting control in the church buildings demonstrate the limited potential for energy savings and that individual areas of the building should be considered and evaluated separately given the varied usage patterns. [ABSTRACT FROM AUTHOR]

In this paper a building occupancy prediction method is presented, which is based on the spatio-temporal analysis of historical data (occupancy modelling) and further relies heavily on current contextual information, being therefore suitable for providing real-time prediction. Two different algorithmic approaches are proposed, based on Markov models, revealing how context awareness adds the capability of rapidly adjusting to current conditions and capturing unexpected events, as opposed to capturing only typical occupancy fluctuation expected on a regular basis. Both proposed approaches are evaluated against accurate real-life data collected from a tertiary building, achieving notable results which outperform currently used methods. [ABSTRACT FROM AUTHOR]

Commercial office buildings represent the largest in floor area in most developed countries and utilize substantial amount of energy in the provision of building services to satisfy occupants’ comfort needs. This makes office buildings a target for occupant-driven demand control measures, which have been demonstrated as having huge potential to improve energy efficiency. The application of occupant-driven demand control measures in buildings, most especially in the control of thermal, visual and indoor air quality providing systems, which account for over 30% of the energy consumed in a typical office building is however hampered due to the lack of comprehensive fine-grained occupancy information. Given that comprehensive fine-grained occupancy information improves the performance of demand-driven measures, this paper presents a review of common existing systems utilized in buildings for occupancy detection. Furthermore, experimental results from the performance evaluation of chair sensors in an office building for providing fine-grained occupancy information for demand-driven control applications are presented. [ABSTRACT FROM AUTHOR]

• A vision-based approach using deep learning architectures to estimate people count. • Two methods that instantaneously and incrementally count people are combined. • The method was tested in a large, crowded and severely occluded classroom. • Results show that our method has high predictive capacity. • Future work should address high computational cost and privacy preservation. Although occupancy information is critical to energy consumption of existing buildings, it still remains to be a major source of uncertainty. For reliable and accurate occupant modeling with minimal uncertainties, capturing precise occupant information on occupants is essential. This paper proposes a computer vision-based approach that utilizes deep learning architectures to estimate of the number of people in large, crowded spaces using multiple cameras. Various vision techniques (head detection, background elimination, head tracking) are implemented in three methods: (i) a method that instantaneously counts people in a scene, (ii) a method that incrementally counts people entering/exiting a room and (iii) a combination of the first two methods. These methods were applied in a classroom with heavy occlusions, and resulted in a high prediction capacity when compared to ground truth measurements. Future work in video-analytical approaches can address problems regarding lowering the computational cost of analysis, capturing occupancy data in complex room geometries and addressing concerns in privacy preservation. [ABSTRACT FROM AUTHOR]

Abstract: This paper illustrates the concept of using dynamic simulation as an instrument for total energy performance validation and analysis for office buildings and their HVAC systems. The intent is to use simulations to establish performance criteria to evaluate monitored data to validate building performance, analyze energy, and to predict the energy consumption during the planning phase. In this study, the total energy use is examined in a real office building modeled using a dynamic simulation. The very comprehensive measured data about energy consumption for each part of the HVAC systems and appliances were compared with the simulation results. The results of this analysis suggest that very good agreement can be achieved using the available precise input data, especially building occupancy patterns and activities. [Copyright &y& Elsevier]

We propose a novel stochastic agent-based model of occupancy dynamics in a building with an arbitrary number of zones and occupants. Simulation of the model yields time-series of the location of each agent (a software representation of an occupant). The model is meant to provide realistic simulation of occupancy dynamics in non-emergency situations. Comparison of the model's prediction of distributions of random variables such as first arrival time of a building is provided against those estimated from measurements in commercial buildings. We also propose a lower complexity graphical model of occupancy evolution in multi-zone buildings. The graphical model captures information on mean occupancy and correlation among occupancy at various zones in the building. The agent-based model can be used in conjunction with building performance simulation tools, while the graphical model is more suitable for real-time applications, such as occupancy estimation with noisy sensor measurements. [ABSTRACT FROM AUTHOR]

Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry today have been developed from either forward or inverse modeling approaches. However, most of these models require extensive computer resources and involve lengthy computation. This paper discusses the use of data-driven intelligent approaches, a probabilistic entropy-based neural (PENN) model to predict the cooling load of a building. Although it is common knowledge that the presence and activity of building occupants have a significant impact on the required cooling load of buildings, practices currently adopted in modeling the presence and activity of people in buildings do not reflect the complexity of the impact occupants have on building cooling load. In contrast to previous artificial neural network (ANN) models, most of which employ a fixed schedule or historic load data to represent building occupancy in simulating building cooling load, this paper introduces two input parameters, dynamic occupancy area and rate and uses it to mimic building cooling load. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing grade A mega office buildings in Hong Kong with tenants including many multi-national financial companies that require 24-h air conditioning seven days a week. The dynamic changes that occur in the occupancy of these buildings therefore make it very difficult to forecast building cooling load by means of a fixed time schedule. The performance of simulation results demonstrate that building occupancy data play a critical role in building cooling load prediction and that their use significantly improves the predictive accuracy of cooling load models. [Copyright &y& Elsevier]

Building occupancy is an important basic factor in building energy simulation but it is hard to represent due to its temporal and spatial stochastic nature. This paper presents a novel approach for building occupancy simulation based on the Markov chain. In this study, occupancy is handled as the straightforward result of occupant movement processes which occur among the spaces inside and outside a building. By using the Markov chain method to simulate this stochastic movement process, the model can generate the location for each occupant and the zone-level occupancy for the whole building. There is no explicit or implicit constraint to the number of occupants and the number of zones in the model while maintaining a simple and clear set of input parameters. From the case study of an office building, it can be seen that the model can produce realistic occupancy variations in the office building for a typical workday with key statistical properties of occupancy such as the time of morning arrival and night departure, lunch time, periods of intermediate walking-around, etc. Due to simplicity, accuracy and unrestraint, this model is sufficient and practical to simulate occupancy for building energy simulations and stochastic analysis of building heating, ventilation, and air conditioning (HVAC) systems. [ABSTRACT FROM AUTHOR]

This work reports the results of a survey carried out on a hall in a historic building in Palermo (Italy) with the aim of assessing the air quality by means of objective measurement of carbon dioxide (CO2) concentration and its subjective evaluation through a questionnaire. The measured values of CO2 concentration were compared with those evaluated using the Meckler equation under steady-state conditions, having verified necessary parameters such as the constancy of the indoor and outdoor CO2 levels and the uniformity of the CO 2 generation rate for all the occupants. The positive correspondence between measured and calculated results allowed us to utilise, under differing circumstances, the values of the natural ventilation rates achieved in steady-state conditions. By using measured and calculated data, an experimental assessment of the relationships between the CO2 concentration levels and the number of occupants and their level of satisfaction was performed. The indoor air quality level in the hall, evaluated by means of the experimental data, was compared with the subjective responses expressed through a specific multiple choice questionnaire handed out to the occupants. A comparison between the calculated ventilation rates and those required by the main American and European standards shows that natural ventilation in the hall is often insufficient according to these standards which have regard for the minimum requirements for air acceptability. In addition, some general observations on the thermal condition of the hall have been drawn from a comparison between microclimate parameters and the subjective opinions provided by the occupants. [ABSTRACT FROM AUTHOR]

The occupancy information in buildings is fundamental for smart buildings (e.g., occupant-centric controls). Opportunistic occupancy detection (OOD) uses connection data of mobile devices. While OOD has been developed and applied, one critical drawback is that it requires the ground truth of occupants to calibrate, which is limited to gather. Here, we introduce CROOD : a capture and recapture (CRc) inspired OOD. In ecology, CRc has been established for the estimation of animal populations, when the manual count is impossible. We adopt this unique approach to estimate the number of mobile devices in a building. Then, using a simple estimate on the total population, CROOD determines the relationship between the numbers of occupants and mobile devices. We evaluate CROOD numerically on the synthetic building populations and demonstrate its application in a university library using WiFi connection data. We find that CROOD can estimate the number of mobile devices and subsequently the number of occupants with 1–2 weeks to converge a reasonable accuracy. A long term experiment shows that CROOD can adapt to varying population characteristics (e.g., occupants bring more mobile devices), outperforming the reference sample mean estimator. The real building implementation demonstrates that while in the first 1–2 weeks, the sample mean estimator is superior, eventually CROOD adapts and provides better estimates without ground-truth calibration. Although CROOD has a limitation of building types and systems, our results envision that CROOD could be a viable addition to other OOD methods to better utilize existing mobile device connection data to estimate occupancy in buildings. • Estimate the ratio between the numbers of occupants and their mobile devices using a capture and recapture estimation. • Computationally light and calibration free method to estimate the building occupancy with 15 days to converge a reasonable accuracy. • An adaptive learning to estimate the temporal changes of the relationship for occupants and their usage of mobile devices. • The synthetic building populations and the algorithm are publicly available for the research area of occupancy estimations. [ABSTRACT FROM AUTHOR]

Understanding the associations of indoor air temperature with building physical characteristics is essential for human comfort and energy saving. While traditional studies depended heavily on simulation tools, statistical approaches have recently provided higher flexibility and efficiency to explore factors associated with indoor air temperature. Under such background, this study systematically constructed building spatial design and occupancy features for 53 university classrooms in 4 teaching buildings in Beijing, China, and used statistical models to quantify their impacts on indoor air temperature. Both multiple linear regression (MLR) and random forest regression (RFR) were conducted for different time periods in a day, and the MLR coefficients and RFR feature importance were examined. 16 spatial design features and 5 occupancy features were identified as significant. Among spatial design features, floor number of the classroom had the most notable positive impacts throughout a day. Window-to-wall ratios from different orientations had distinctive influences and added up to make a huge difference. Impacts from classroom size and position, parent building's forms and envelopes were certain but less important. Among occupancy features, it was found for teaching buildings that the whole-buildings' occupancy had stronger impacts over the classrooms' one, and daily occupancy over temporal one, with building daily occupancy rate being the most influential. The results highlight the potential to adjust thermal environment through proper organization of spatial design and occupancy features addressing their rankings. The RFR models obtained adjusted R2 of above 98%, testifying the effectiveness of predicting indoor air temperature with well-selected building features. [Display omitted] • 16 spatial design and 5 occupancy features were related to indoor air temperature. • Impacts of these features were quantified and ranked using statistical models. • Floor number of the classroom was the most influential spatial design feature. • Building daily occupancy rate was the most influential occupancy feature. • The random forest regression models obtained notable performance. [ABSTRACT FROM AUTHOR]

Abstract: It is necessary to know CO2 source generation rates and system flow parameters, such as supply flow rate and overall room ventilation effectiveness, in order to evaluate cost savings for demand-controlled ventilation applied to commercial buildings. This paper presents a methodology for estimating schedules for generation rates and flow parameters using short-term testing. These parameters are used within a model that predicts return air CO2 concentrations as part of an overall energy analysis model. As a first step in developing the methodology, two different parameter estimation techniques were evaluated using simulated data. Each method gave models that provide good predictions of return air CO2 concentrations, but differed in terms of the identified parameters. The preferred parameter estimation method provides estimates of both average hourly source generation rates and day-to-day variations. This technique was applied to three different types of commercial buildings using field monitored data. The sites are small commercial buildings with packaged HVAC equipment and included modular schoolrooms, children''s play areas in fast food restaurants and a pharmacy retail store. The impact of the length of model training data period on estimated CO2 generation rates was investigated. Eight weeks of data is sufficient for training. Expressed in terms of the coefficient of variation, the errors in predicted CO2 concentrations ranged from 4% to 15% depending on the sites. The predicted frequency of time that CO2 concentrations were within a given range agreed well with the field measured data. [Copyright &y& Elsevier]

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• A novel model is proposed for short-term building occupancy prediction. • The proposed model achieves better performance over conventional methods. • MOTP can reflect occupancy's status interdependency and stochastic characteristics. • Models with deep and ensemble structures are preferable to taking advantage of MOTP. • Important factors influencing the model performance are investigated and assessed. Occupancy plays a vital role in optimizing the operation of building service systems. This study proposed a novel model for predicting short-term building occupancy. In the model, the autocorrelation function (ACF) and partial autocorrelation function (PACF) are used to determine the most relevant occupancy status. Then the multi-order transition probability (MOTP) is established and integrated with the deep forest (DF) for occupancy prediction. To assess the effectiveness of the proposed MOTP-DF model, a validation experiment was conducted in an office room to compare its prediction performance with conventional methods, including Markov chain, decision tree (DT), and support vector machine (SVM) models. The results show that the proposed model can track occupancy change with higher accuracy and fewer fluctuations. Moreover, it improves the prediction accuracy by 6.3–10.0%, 4.6–8.3%, and 4.8–8.3% over the Markov chain, DT, and SVM models, respectively. A further evaluation indicates that MOTP can quantitatively incorporate occupancy's status interdependency and stochastic characteristics into its prediction. Compared with the DT and SVM models, the DF model with deep and ensemble structures could benefit more from the integration with MOTP due to its higher robustness. This study also found that a proper selection of transition probability orders, forest algorithms, and corresponding maximum tree depths can further enhance the prediction accuracy of the proposed model. [ABSTRACT FROM AUTHOR]