Occupancy Schedules Research Articles

Field measurement on the particle concentrations and exposures in five residential buildings in Korea

This study measured five apartments in the Daejeon. There is higher fluctuation for ambient air particle conditions. The average PN (particle number) concentrations was 5.2x108/m3. There are differences between five sites because of fluctuation for ambient air conditions. Particle concentration has different each occupant schedule. I/O ratios were different by occupancy schedules. Indoor particles exceeded 0.7 μm was significant emitted indoor activities. This result comes from two reasons. 1) Penetration by envelopes. 2) Indoor sources by occupants’ activities. PN decay rate (a+k) were 0.49-4.29 h−1 during indoor peak events. Total emitted particles (σ) was 0.0136.15 x 1013 particles per events. The cooking generated PN were wide range because of different event conditions. The ventilation rate is not enough to remove particles.

Sensitivity analysis of control strategies for mechanical ventilation in a low-energy apartment building

Building simulation tools are increasingly used during design of new as well as refurbishment of residential buildings. However, reliability of simulation is highly dependent on its inputs. The project investigated application of sensitivity analysis on input parameters for simulation of ten different residential ventilation control strategies. Nine strategies comprised demand control (DCV) one used constant air volume. Demand was represented by operative temperature, CO2, relative humidity or their combinations. They were measured either in ventilation exhaust or in particular rooms. Primary energy consumption and quality of indoor environment were evaluated. A low-energy apartment of 93.8 m2 placed in Nordhavn, Copenhagen, Denmark was used as a case study. Investigated input parameters were: heating set-point, occupancy schedule, window opening, internal heat and moisture gains, glazing area, g-value, solar shading and night ventilation. The control strategy capable of providing the best indoor climate (DCV with combination of sensors in individual rooms) was at the same time the most energy demanding. The sensitivity analysis revealed that heating setpoint and window opening were the most crucial input parameters with respect to primary energy consumption. Window opening was also the most influential factor with respect to overheating and moisture. Occupancy had a strongest effect on CO2.

Simulation and analysis of load shifting and energy saving potential of CO2-based demand-controlled ventilation in a sports training center

This paper aims to evaluate and characterize the impact of optimizing the operation of the HVAC system through maintaining dynamic CO2-based Demand-Controlled ventilation (DCV) on the electricity load profile and energy consumption of the sports training center of Leibniz University Hannover. The actual ventilation control scheme, in which the operation of the HVAC system is operated with a two-stage volume flow controller based on indoor CO2 concentration is improved through two steps to avoid overventilation and reduce power consumption. For this purpose, a detailed multi-zone model of the sports center and energy supply system has been developed in TRNSYS. In the first step, a multi-stage control scenario is implemented considering the occupancy schedules and indoor CO2 concentration measurement data. In the second step, based on an indoor CO2 concentration model, a predictive control scenario is developed and applied. Aiming at characterizing the influence of these operation scenarios on the power consumption of the building, the annual electricity load profiles of the simulation cases will be analyzed and compared with the actual load profile of the building based on the technical planning documents and data provided by building management system (BMS). Simulation results show that utilizing predictive CO2-based DCV leads to a reduction of the peak load electricity by almost 2 kW and the base load by 5 kW as well as decreasing the annual energy consumption by 40 %.

A systematic development and validation approach to a novel agent-based modeling of occupant behaviors in commercial buildings

Occupant behaviors are one of the dominant factors that influence building energy use. Traditional building energy modeling programs use typical occupant schedules that often do not reflect actual situations. Robust occupant behavior modeling that seamlessly integrates with building energy models will not only improve simulation performance, but also provide a deeper understanding of occupant behaviors in buildings. This paper presents a development and validation approach to a novel occupant behavior model in commercial buildings. A robust agent-based modeling (ABM) tool, namely Performance Moderator Functions server (PMFserv), is used as the basis of the occupant behavior model. The ABM considers various occupant perceptions and interactions with window, door, and window-blinds based on the environmental conditions. An elaborate agent-based model that represents an office space in an existing building is developed. This is followed by a validation study of the ABM through the use of embedded sensors that capture the indoor ambient conditions and a survey to record actual occupant behaviors. By comparing the recorded behavior data with ABM output, this paper discusses the proposed ABM's prediction ability, limitations, and extensibility. Finally, the paper concludes with the potential of integrating the occupant behavior model with building energy simulation programs.

A probabilistic approach toward achieving net-zero energy buildings using a stochastic office tenant model

During the simulation-aided net-zero energy (NZE) building design process, there is major uncertainty related to occupancy schedules, occupant behavior, and the corresponding effect on thermal and electrical loads in buildings—particularly as building envelopes, lighting, and mechanical systems become more efficient. As a result, designers’ assumptions about occupant-related energy use may have a significant impact on equipment sizing and the associated capital cost. To demonstrate a design process that considers the relationship between uncertainty from occupants and corresponding costs, this paper used a stochastic office tenant model (occupancy, lighting, and plug loads) for photovoltaic (PV) system sizing of an NZE building. Using the medium Canadian office reference building model as a case study, it was found that uncertainty of occupants for sizing PV is very expensive if the objective is to be highly confident that the building will be NZE. The results revealed that each incremental percentage point of improved certainty toward achieving NZE becomes increasingly expensive.

Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes

Building stock modelling usually deploys representative building archetypes to obtain reliable results of annual energy heating demand and to minimise the associated computational cost. Available methodologies define archetypes considering only the physical characteristics of buildings. Uniform occupancy schedules, which correspond to national averages, are generally used in archetype energy simulations, despite evidence of occupancy schedules which can vary considerably for each building. This paper presents a new methodology to define occupancy-integrated archetypes. The novel feature of these archetype models is the integration of different occupancy schedules within the archetype itself. This allows building stock energy simulations of national population subgroups characterised by specific occupancy profiles to be undertaken. The importance of including occupant-related data in residential archetypes, which is different than the national average, is demonstrated by applying the methodology to the UK national building stock. The resultant occupancy-integrated archetypes are then modelled to obtain the annual final heating energy demand. It is shown that the relative difference between the heating demand of occupancy-integrated archetypes and uniform occupancy archetypes can be up to 30%.

Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches

As the cooling energy demand in buildings is set to increase dramatically in the future, the exploitation of passive solutions like natural ventilation could prove vital in reducing the reliance on mechanical systems. Models that can predict air temperature accurately in naturally ventilated mode are key to understanding the potential of natural ventilation now and in the future. This article presents a simulation based case study of a retrofitted nearly zero energy test-bed university building, in naturally ventilated mode only. The study had three aims: (1) calibration and validation of a whole building energy model, (2) a comparative analysis of occupancy schedules and opening control strategies, and (3) a comparison of researcher and practitioner approaches. Results showed the detailed building model was capable of predicting room level air temperature with a low level of error (0.27 °C ≤ RMSE ≤ 1.50 °C) that was well within the limits of existing calibration standards (MBE ±10%, CVRMSE <20%). The comparative analysis highlighted the need to consider occupancy schedules that have a wide range of diversity, and opening control strategies that reflect the manual and automated relationship in natural ventilation systems. The approach comparison highlighted that both practitioner and researcher approaches to simulating both occupancy schedules and opening control strategies showed similar levels of performance for the application considered. The paper also provides recommendations for those modelling air temperatures and thermal comfort in nearly zero energy buildings.

Comparative study of data driven methods in building electricity use prediction

The energy use prediction of building systems is crucial to design a high-efficiency building and maintain low energy consumption operation, which is also important in optimizing building system control and retrofitting. This paper demonstrates a comparative study of four data-driven methods used in online building energy predictions involving large-scale data extracted from several types of buildings. The characteristics of building electricity use and data reliability were addressed through the data pre-treatment process including visualization, cleaning, parsing and filtering. Mathematical algorithms and their applications in previous studies were summarized and compared, and evaluation methods were developed. The performance and suitable application scenarios of the proposed algorithms were conducted via the comparison of monitoring data and predicted results. The study indicates that the most complex method which requires the highest computation ability, i.e., the Artificial Neural Network (ANN), does not lead to the highest accuracy, while as the fastest computation method, Gaussian Process Regression (GPR) usually has the results with the lowest accuracy. Support Vector Machine (SVM) and Multivariate Linear Regression (MLR) methods usually perform better in the case scenarios studied. All the prediction accuracies can meet the requirements of RMSE <30% and NMBE <10% proposed by ASHRAE, and the computation time varies from less than 1 s to 22 s per prediction. All these methods/algorithms worked well for buildings with stable energy use patterns. For buildings with complex and unstable occupancy schedules and energy use patterns, MLR and SVM methods have the ability to achieve a high accuracy with fast computation speed.

Investigation and analysis of Chinese residential building occupancy with large-scale questionnaire surveys

Occupancy of residential buildings is one of the most important inputs for building simulations, building system design, optimization, and energy efficient policy-making. There are a few large-scale surveys of China's urban residential occupancy that help explain the overall situation and occupancy profile. In this study, large-scale surveys of urban households were conducted in four Chinese cities, totaling 3424 valid samples. Descriptive analysis, clustering analysis, and correlation analysis were then conducted on the dataset to ascertain the typical occupancy schedules.The conclusion derived from the large-scale occupancy data is that the occupancy rate of urban residential buildings is low throughout much of the day and in many of the individual rooms. Four typical clusters were identified for each room type in a residential building. Regional differences exist for a typical cluster share, but no seasonal difference was observed. Because of partial usage of residential buildings, building services should reflect this partial usage and policy-makers should consider adopting energy policies consistent with this real occupancy schedule.

Mapping the energy performance gap of dwelling stock at high-resolution scale: Implications for thermal comfort in Portuguese households

An aging building stock and low-efficiency heating, ventilation and air conditioning (HVAC) systems may be preventing the adequate energy performance (EP) of Portuguese dwellings. This study aims to estimate and analyze the EP gap of the occupied main residence Portuguese residential dwelling stock, for thermal comfort attainment, at high geographical resolution scale, i.e. for every civil parish. A building typology approach was applied to estimate the heating and cooling (H&C) theoretical final energy consumption (TFEC) for thermal comfort. An energy consumption statistics-based approach was used to estimate the H&C real final energy consumption (RFEC). The EP gap is the percentual difference between the TFEC and RFEC. Three scenarios were tested, looking deeper into southern European space H&C patterns, considering varied conditioned areas and occupancy schedules. This study provides a methodological framework for zooming in the assessment of the dwelling stock EP for a whole country, allowing for a comparative analysis between regions, as the different regional EP gap drivers are identified. For nominal conditions, every civil parish has an EP gap higher than 60%, for both H&C, related to poor energy efficiency of the building stock and low H&C energy consumptions. The scenarios demonstrated a bridging of several civil parishes’ EP gap, possibly resulting from temporal space climatization patterns. High remaining EP gaps suggest the civil parishes in the north and center inland regions are the most vulnerable in the winter and summer seasons, potentially due to significant energy poverty levels.

I-BLEND, a campus-scale commercial and residential buildings electrical energy dataset

Efficient energy consumption at the building level is vital for sustainability. Providing energy efficient systems and solutions requires an understanding of how energy gets consumed. However, there is a general lack of large-scale open datasets about the energy consumption of buildings, which hinders the research. The recent emergence of smart energy meters makes it possible to collect such data, which can then be used for analysis. In this paper, we release I-BLEND, 52 months of electrical energy dataset at a one-minute sampling rate from commercial and residential buildings of an academic institute campus in an emerging economy, India. Also, we provide occupancy datasets at a 10-minute sampling rate for each of the campus buildings. To the best of our knowledge, this is the first such dataset from India. Public availability of such fine-granular data will allow users to perform different research tasks such as analyzing the impact of weather or occupancy schedule on energy consumption, detecting anomalies, and developing algorithms for predictive maintenance.

A conceptual framework for the real-time monitoring and diagnostic system for the optimal operation of smart building: A case study in Hotel ICON of Hong Kong

This study proposes a conceptual framework for the real-time monitoring and diagnostic system for the optimal operation of smart building, focusing on the energy-efficient, occupant-oriented, and comfortable indoor environment. The proposed framework aims to improve the energy efficiency in a room of building while achieving the healthy and comfortable indoor environmental quality and occupants’ satisfaction. The proposed framework consists of a three-phase cyclic process (i.e. monitoring, diagnostic, and intervention), and it can be simply replicated and extensively applied to the different levels of physical entities (spatial scalability) in the different time resolutions in the whole life cycle processes (temporal scalability). To elaborate the feasibility of the proposed framework, the Hotel ICON in Hong Kong are chosen as a case study. For three rooms in the Hotel ICON, several sensors are installed for monitoring the energy efficiency and indoor environmental quality in real time. With the collected dataset, it is planned to carry out the diagnostic process (e.g. anomaly detection, time-series analysis, and occupancy schedule pattern analysis) and the intervention process (e.g. automatic control, occupant behaviour change, and optimal operation). The conceptual framework provides a standardized and systematic research approach toward a big picture of an intelligent building systems for the energy-efficient, occupant-oriented, and comfortable indoor environment.

Effectiveness of cool walls on cooling load and urban temperature in a tropical climate

The urban overheating, driven by the increasing expansion of our cities and the global climate change, is becoming one of the main environmental challenges of today. Consequently, cooling technologies are emerging as mitigation and adaptation strategies. Reflective roof and pavement surfaces have been widely studied for their potential benefits, but detailed evaluations of the effect of wall albedo on the urban microclimate are limited. This study addresses this gap by evaluating the effects of reflective walls on urban energy use and outdoor climate. The energy balance of an idealized neighborhood is represented using a 3D numerical model, Temperature of Urban Facets Indoor-Outdoor Building Energy Simulator (TUF-IOBES), which determines the cooling loads and outdoor air temperature. The study focuses on the tropical climate of Singapore, addressing the urban climate in highly-populated cities in low latitudes that are significantly affected by the UHI. Simulations are conducted for two neighborhoods representative of low-rise residential and high-rise commercial urban areas, spanning a range of urban density, canyon geometry, building construction, and occupant schedules. The building thermal load and outdoor temperature are then calculated for these two idealized neighborhoods, analyzing the effectiveness of cool walls while also considering the role of other design factors such as window-to-wall ratio (WWR) and glazing solar heat gain coefficient (SHGC) in modulating the impact. Unlike the analysis of cool roofs, we find that a universal conclusion regarding the impact of cool walls cannot be drawn. The role of wall albedo significantly depends on the collective design of urban areas as well as the use and occupancy of buildings. We find that urban density (in other words the local climate zone) followed by window properties are important factors in determining the impact of wall albedo on thermal loads and UHI, as they determine the radiative exchange between and into the buildings. Accordingly, contrary to the general expectation, for a high urban density (commercial neighborhood LCZ6) and high WWR and SHGC, we observe that cool (reflective) walls can increase the building energy use. Regarding UHI, increasing the reflectivity of walls decreases the canopy air temperature but the impact is marginal ( ∼ 0.1 °C) compared to other urban design parameters.

Real-Time Occupancy Estimation Using WiFi Network to Optimize HVAC Operation

Commercial and residential buildings consume about 27% of total energy used in the US, out of which nearly half is consumed by commercial building sector and it expected to grow in the next 30-year period. Literature suggests that occupancy data may improve the energy consumption of the buildings, especially in HVAC operation. In the past few years studies came up with various frameworks based on existing infrastructure to estimate occupancy, out of which commodity WiFi gained popularity in detecting, estimating, and tracking occupants within buildings. However, there are concerns with those frameworks such as added infrastructure and computational efforts, upgrades to existing infrastructure, and privacy of occupants. This paper presents a simplistic framework based on commodity WiFi to estimate real time occupancy data without any added infrastructure or upgrades, while protecting the occupant privacy and can produce significant energy reduction in HVAC operation. The framework is tested on a large lecture hall in an institutional building that has multiple classes scheduled. The initial tests showed that the WiFi based occupancy had a 0.96 correlation with the established ground truth. Additionally, the WiFi based occupancy schedule resulted in at least 50% savings in HVAC energy consumption over static schedule.

Radon risk analysis in a set of public buildings in Minho region, Portugal: from short-term monitoring to radon risk assessment

The World Health Organization (WHO) considers radon gas as one of the most important agents responsible for lung cancer. Hence, radon people exposure in badly ventilated and extensively occupied rooms increases drastically the risk of health problems. Since new legislation is already in force in Portugal to combat radon risk problems, the discussion of a mitigation strategy to reduce occupants’ radon risk exposure is an issue of big actuality, of greater urgency and of extreme necessity.An administrative building made with granite stonework and founded over granite rocks, located in an inner city of Minho region, North of Portugal, was extensively assessed to characterize indoor radon concentration, air temperature and relative humidity in two office rooms, a ground floor and a top floor room. The ground floor office has numerous occupants during large periods, and the radon risk analysis showed hazard concerning radon gas exposure. The experimental assessment involved the use of short-term measurements of one complete week, over winter and summer seasons.The Indoor Effective Dose (IED) calculation, considering a dosimetric approach for the room occupation schedule, allowed concluding that during all working day, the office was above the occupational dose limit of 1 mSv/year, prescribed by the International Commission on Radiological Protection (ICRP). According to results, there is evidence that the occupants’ radon risk exposure is very high, wherefore some remediation measures are mandatory. The room mitigation actions should necessarily involve a set of technical solutions to reduce the potential risk for occupants combined with some awareness-raising activities to alert for risk occupation during large periods. Thus, a radon mitigation strategy is obligatory in order to provide occupants´ protection.

Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data

Purpose Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data. Design/methodology/approach In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario. Findings It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification. Originality/value In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.

An investigation of formaldehyde concentration in residences and the development of a model for the prediction of its emission rates

Indoor air pollution caused by formaldehyde associated with building materials imposes a variety of acute and chronic adverse effects on people's health. The aim of this research is to investigate the concentrations of formaldehyde in residences and develop emission rate prediction model in residential buildings. On-site measurements including the indoor and outdoor concentrations of formaldehyde and CO2 were carried out in 42 urban residences in Chongqing. The people occupancy schedule in different functional rooms was obtained by observing the change in CO2 concentration. A robust model for the estimation of formaldehyde emission rates using CO2 as the tracer gas; associated with a Monte-Carlo simulation of occupant activities and the characteristics of residences; has been developed. It is revealed that the mean indoor formaldehyde concentration was 30.12 μg/m3, which was slightly higher than the outdoor concentration of 27.80 μg/m3. The emission rates of 61.82 ± 52.39 and 49.69 ± 42.13μg/h/m2 (mean ± SD) during the daytime and nighttime, respectively with a daily average of 57.20 ± 48.79μg/h/m2. The significant contribution to indoor formaldehyde concentration was from indoor sources. Indoor formaldehyde source control is suggested to be an efficient way to control the indoor concentration.

Analysis of Accuracy Determination of the Seasonal Heat Demand in Buildings Based on Short Measurement Periods

In this paper, we present a multi-variant analysis of the determination of the accuracy of the seasonal heat demand in buildings. The research was based on the linear regression method for data obtained during short periods of measurement. The analyses were carried out using computer simulation, and the numerical models of the multifamily building and school building were used for the simulation. The simulations were performed using the TRNSYS, ESP-r, and CONTAM programs. The multi-zone models of the buildings were validated based on the measurement data. The impact of the building’s parameters (airtightness, insulation, and occupancy schedule) on the determination of the accuracy of the seasonal heat demand was analyzed. The analyses allowed guidelines to be developed for determining the seasonal energy consumption for heating and ventilation based on short periods of heat demand measurements and to determine the optimal duration of the measurement period.

Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach

Load management actions in large buildings are pre-programmed by field engineers/users in the form of if-then-else rules for the set point of the thermostat. This fixed set of actions prevents smart zoning, i.e. to dynamically regulate the set points in every room at different levels according to geometry, orientation and interaction among rooms caused by occupancy patterns. In this work we frame the problem of load management with smart zoning into a multiple-mode feedback-based optimal control problem: multiple-mode refers to embedding multiple behaviors (triggered by building-occupant dynamic interaction) into the optimization problem; feedback-based refers to adopting a Hamilton-Jacobi-Bellman framework, with closed-loop control strategies using information stemming from building and weather states. The framework is solved by parameterizing the candidate control strategies and by searching for the optimal strategy in an adaptive self-tuning way. To demonstrate the proposed approach, we employ an EnergyPlus model of an actual office building in Crete, Greece. Extensive tests show that the proposed solution is able to provide, dynamically and autonomously, dedicated set points levels in every room in such a way to optimize the whole building performance (exploitation of renewable energy sources with improved thermal comfort). As compared to pre-programmed (non-optimal) strategies, we show that smart zoning makes it is possible to save more than 15% energy consumption, with 25% increased thermal comfort. As compared to optimized strategies in which smart zoning is not implemented, smart zoning leads to additional 4% reduced energy and 8% improved comfort, demonstrating improved occupant-building interaction. Such improvements are motivated by the fact that the approach exploits the building dynamics as learned from feedback data. Moreover, the closed-loop feature of the approach makes it robust to variable weather conditions and occupancy schedules.

A simulation-based optimization method for designing energy efficient buildings

Buildings have a remarkable impact on the environment, therefore finding efficient design configurations satisfying conflicting criteria such as, economic and environmental performance has become an important task. The aim of this study is to propose a methodology to aid designers in satisfying the requirements of government regulations and green building certification programs, while optimizing the energy consumption and maintaining the thermal comfort. In this context, a software package combining a tailor-made thermal simulation software and Matlab Optimtool is developed to implement proposed methodology. The developed software package offers an effective method to perform large number of simulations to find optimal building configuration. The software package was tested on a reference building that represents a typical residential building in Turkey. The testing process is conducted on a five story building located in Istanbul and building information such as building size, location and occupation schedule were used within the developed software. Based on an extensive market search for building materials, cost functions for each parameter were developed as well as a database required for optimization process. A genetic algorithm optimization technique was utilized to minimize the objective function and find the optimal building configuration for the selected building. Development of a simulation-based optimization method fulfills the need for a tool that assists designers to find better design alternatives at the conceptual design stage. The software package requires least amount of data input for energy simulation process to improve usability. A tailor-made energy simulation module was developed to significantly reduce optimization time period. Besides, instead of coupling two separate software packages, performing the energy simulation and optimization processes on a single platform (MATLAB) reduces the time required to find optimal design and eliminates compatibility issues. Developing simulation-based optimization software on a single platform increased the flexibility and user-friendliness of the software. The effectiveness of the approach for finding optimal building configuration is demonstrated in the presented test cases.