Indoor Environmental Data Research Articles

Highly sensitive measurement of trace methane gas using mid‐infrared tunable diode laser absorption spectroscopy method

AbstractTo monitor the concentration of harmful gas methane (CH4) in the tunnel in real time, a high‐sensitivity measurement method for CH4 trace gas based on tunable diode laser absorption spectroscopy (TDLAS) is designed in this paper. An interband cascade laser with the emission wavelength of 3.29 μm was used to target noninterference CH4 lines at 3038.5 cm−1. To diminish the background noise of the sensor system, wavelength modulation spectroscopy combined with a second harmonic measurement technique was used in this work. Finally, 1.83 parts per billion in volume (ppbv) minimum measurement limit (MML) was obtained with a 4 s integration time based on an Allan‐Werle variance analysis, and the MML can be further improvedto ∼0.4 ppbv by increasing the integration time up to 104 s. Indoor and outdoor environmental data of CH4 acquired using the TDLAS sensor system are also reported.

Personalized federated learning on NLOS acoustic signal classification

AbstractTo identify non‐line‐of‐sight, acoustics‐based indoor positioning needs to collect audio recordings of sound fields in multiple rooms and upload them to the central server for training. However, if the transmission process or the server‐side suffers from malicious attacks, private data may be leaked. To address both training difficulties and privacy issues simultaneously, a novel model for personalized federated learning is proposed. This model incorporates user frequency and room data capacity while also taking into account significant differences in positioning data and room layout. The proposed model can accurately identify differences between various room data during aggregation on the server‐side. By collecting data in actual indoor environments and comparing it with existing algorithms, the proposed method achieved 90% accuracy in verifying data for unfamiliar rooms.

Analysis and prediction of carbon emission in the large green commercial building: A case study in Dalian, China

Reducing carbon emissions from the construction industry has been vital to addressing the growing global environmental change challenge. Building energy consumption data is crucial to many applications, such as carbon emission auditing, energy efficiency improvement, etc. This study aims to mine the energy consumption patterns of commercial buildings and explore the applicability of a data-driven model in building carbon emissions prediction. This research selected Dalian's large-scale green commercial building as a case study. The electrical load data for the past four years were collected, and the indoor and outdoor environmental data were monitored under different seasons. Machine learning was used to develop building carbon emissions forecasting models. The average annual increase rate of building electricity consumption before the pandemic was 5.9%. Miscellaneous electric loads (MELs) is the largest electricity consumer in the target building. On typical days, indoor illuminance and CO2 are highly correlated under different seasons. A forecasting model based on ensemble learning is found to have certain advantages in building carbon emissions prediction.

Mental-state estimation model with time-series environmental data regarding cognitive function

It is expected that understanding and estimating the human mental state will be helpful for mental health measures, improving learning and labor work efficiency, and preventing human error. Our research group has been developing a methodology and model for estimating the mental state of humans in their environment from indoor environmental data regarding cognitive function obtained by wireless sensor network technology. This study constructed a model to estimate mental states from multidimensional time-series indoor environmental data such as temperature, humidity, and illumination. The experiment results show that the proposed system shows higher estimation accuracy. In particular, the CNN-LSTM model with multidimensional time-series indoor environmental data shows about 90% estimation accuracy. The results obtained in this study, showing that mental states can be determined with high accuracy from environmental data, are helpful for future research approaches. It may also contribute to the creation of a less stressful environment.

Integrating Logis Regression and XGBoost to Construct Indoor Air Quality Improvement Research

In the face of the severe global epidemic, indoor architectural space has become one of the critical issues, and the construction of a new type of “built environment” while solving “health and epidemic prevention” has become the goal of active development in countries around the world (SDGs & Pandemic Response); Pollutant concentration, optimization of indoor heat and humidity environment, and release of indoor environmental monitoring data, etc. It can not only protect the short-term needs of building users but also provide long-term health protection for building users and ultimately achieve the purpose of physical and mental health of building users. This study uses GIA-K007-12 Air Box to collect “environmental characteristics” variables; IAQ, PM1, PM2.5, PM10, CO2, TVOC, HCHO, Fungi index, TEMP, and HUMD are input variables for XGBOOST, using IBM SPSS Statistics 20.0 performs statistical analysis, modelling and using PYTHON to simulate the accuracy of the building fresh air system model and the decision ranking of essential factors. The test results are based on the XGBOOST decision tree. The accuracy value reaches 94.24%, and the order of critical environmental factors for the indoor fresh air system is PM1, HCHO, IAQ, Fungi index, TVOC, etc. The research results can provide the basis for constructing a teaching space for epidemic prevention and demonstrate that the establishment of an “air quality control platform that can be calculated in real-time” can improve the environmental health awareness (EHL) of stakeholders and provide for future development of epidemic prevention space planning and design in the post-epidemic era Reference and application of operation management.

Occupancy Modeling on Non-intrusive Indoor Environmental Data Through Machine Learning

Occupancy Modeling on Non-intrusive Indoor Environmental Data Through Machine Learning

Avaliação de conforto término em estações de trabalho localizadas próximo a fachadas envidraçadas: modelos de ajuste solar

Abstract Although solar radiation exposure strongly impacts people's thermal comfort, workstations are frequently positioned close to glazed façades. Solar adjustment models have been developed to include the solar radiation impact on people's thermal comfort, which the Fanger and Adaptative models do not address. Nonetheless, those models present differences in their calculation procedures, which have not been thoroughly investigated. This study aims to verify differences in thermal comfort results by applying various solar adjustment models of thermal comfort indices for a person sitting close to a highly glazed façade in an office room. The analyses were based on indoor environment data from computer thermal simulations in EnergyPlus for a temperate climate in Brazil. The results for the same room demonstrated significant differences. For example, the hourly difference between the solar-adjustment models reached 1.7 in PMV, and the MRT was up to 17.6 ºC higher than the mean result calculated through EnergyPlus. The need for solar radiation incidence control is also reinforced. Although the results were not compared with measurement data or people's thermal perception, the limitations and advantages of each solar adjustment model were numerically analysed, broadening the discussions about the solar adjustment models and the consequences of the choices among them.

Indoor Thermal and Ventilation Indicator on University Students’ Overall Comfort

Thermal comfort (TC) and CO2 concentration significantly influence the overall indoor comfort sensations of building occupants. However, few studies have focused on educational buildings regarding both TC and CO2 concentration in tropical regions, and they also lack guidelines for short-term evaluation, which is essential for university classrooms. In this study, a mechanically ventilated university classroom was selected to investigate the 5 min-averaged comfort ranges for indoor parameters and the impacts of TC and variation of CO2 on student overall comfort. The real-time indoor environmental parameters were monitored, including indoor air temperature (Ta), mean radiant temperature (Tm), relative humidity (RH) and CO2 and air velocity (va); the operative temperature (Top) was calculated. Moreover, an online-based questionnaire survey related to thermal sensation (TS) and CO2-related air sensation (AS) was carried out. Linear and nonlinear regression models of comfort sensation predictions were obtained based on the questionnaires and corresponding measured indoor environmental data. The 5 min-averaged comfort ranges for Top, CO2 and RH are 21.5–23.8 °C, <1095 ppm and 47–63.5%, respectively. The comfort range of the TS and AS are 2.3–3.1 and 1–1.55, respectively. The result shows that students prefer a relatively cold indoor environment, as this improves their ability to tolerate bad indoor air quality (IAQ) with high CO2. A regression analysis indicated that AS is the most critical aspect, with a weight of 0.32, followed by TS, with 0.18. Finally, it was also found that individual weighting coefficients were not equivalent and differed across geographical locations and building types. Thus, obtaining the prediction models for a particular building is necessary. The results can give meaningful suggestions to adopt the appropriate operations for HVAC and improve indoor environmental quality in university buildings in tropical regions.

A mixed-methods case study on resident thermal comfort and attitude towards peak shifting of space heating

Recent simulation-based studies have indicated that Economic Model Predictive Control (EMPC) of space heating systems can exploit the thermal mass in residential buildings for demand response (DR) purposes in district heating systems. However, there is a lack of studies on residents’ perception and acceptance of the fluctuating indoor air temperature behaviour inherent in EMPC of space heating. This paper reports on a case study featuring the residents of three one-story houses located in Denmark. The houses were equipped with technology enabling remote actuation of radiator thermostats and collection of various indoor environmental data. Four different temperature boost interventions mimicking the typical behaviour of EMPC of radiators were executed while a mixed-methods triangulation design, employing questionnaires and semi-structured interviews, was used to collect subjective data. Data showed that residents accepted the behaviour but only after the benefits of the EMPC were explained. The acceptance was motivated by either the prospect of saving money, environmental benefits, or a combination of both. The results thereby indicate that explaining the benefits of EMPC is important if residents are to abandon their current preferences and practices in favour of an EMPC concept. The explanation should consider the diversity in current preferences, expectations, and level of technological pre-knowledge. In addition, the paper also reports on a range of findings related to technical aspects of realising EMPC in practice.

Measured Indoor Environmental Data in a Retrofitted Multiapartment Building to Assess Energy Flexibility and Thermal Safety during Winter Power Outages

The article describes detailed measurements of indoor environmental parameters in a multiapartment housing block located in Milan, Italy, which has recently undergone a deep energy retrofit and is used as a thermal battery during the winter season. Two datasets are provided: one refers to a series of experimental tests conducted by the authors in an unoccupied flat, in which the thermal capacity of the building mass is exploited to act as an energy storage. The dataset reports, with a time step of 10 min, measurements of air temperature, globe temperature and surface temperatures in the analyzed room and data characterizing the adjacent spaces and the outdoor conditions. The second set of data refers to the air temperature monitoring carried out continuously in all the apartments of the apartment block, and hence also during two unplanned heating power outages. The analyzed data show the role of deep renovations in extending the time over which a building can remain in the thermal comfort range after an energy interruption and thus highlight the potential role of retrofitted buildings in delivering energy flexibility services to related stakeholders, such as the occupants, the building manager, the grid operator, and others. Furthermore, the dataset can be used to calibrate an energy simulation model to investigate different demand-side flexibility strategies and evaluate thermal safety under extreme weather events.

Grey-box Model Develop and MPC Simulation of Campus Office Zone Based on Actual Measurement

This study aimed to develop a grey-box building model in a zone of a high-rise building on a university campus and to simulate model-based predictive control. Grey-box modeling was performed using various indoor and external environment data measured within a single-person office space on a high floor. The residence schedule was indirectly calculated using values such as indoor CO2 concentration and plug power. In addition, the catalog rating value was applied as the cooling energy value of the indoor 4-ways air conditioner as this could not be measured. Measurements were performed for approximately 4-weeks, and operating data of the heat pump and non-operating (i.e., free-floating) data were obtained according to the weekday and weekend schedules. The grey-box model was parameterized using the linear time-invariant and linear time-variant models. Both models showed good predictive performance with a root mean square error of less than 1°C in the 2-week estimation and 2-week validation results. Based on these results, an MPC simulation was performed. MPC simulation in a single zone were conducted for various electricity cost structures to derive quantitative electricity cost savings compared to those of feedback control.

Validation of Non-Restrictive Inertial Gait Analysis of Individuals with Incomplete Spinal Cord Injury in Clinical Settings.

Inertial Measurement Units (IMUs) have gained popularity in gait analysis and human motion tracking, and they provide certain advantages over stationary line-of-sight-dependent Optical Motion Capture (OMC) systems. IMUs appear as an appropriate alternative solution to reduce dependency on bulky, room-based hardware and facilitate the analysis of walking patterns in clinical settings and daily life activities. However, most inertial gait analysis methods are unpractical in clinical settings due to the necessity of precise sensor placement, the need for well-performed calibration movements and poses, and due to distorted magnetometer data in indoor environments as well as nearby ferromagnetic material and electronic devices. To address these limitations, recent literature has proposed methods for self-calibrating magnetometer-free inertial motion tracking, and acceptable performance has been achieved in mechanical joints and in individuals without neurological disorders. However, the performance of such methods has not been validated in clinical settings for individuals with neurological disorders, specifically individuals with incomplete Spinal Cord Injury (iSCI). In the present study, we used recently proposed inertial motion-tracking methods, which avoid magnetometer data and leverage kinematic constraints for anatomical calibration. We used these methods to determine the range of motion of the Flexion/Extension (F/E) hip and Abduction/Adduction (A/A) angles, the F/E knee angles, and the Dorsi/Plantar (D/P) flexion ankle joint angles during walking. Data (IMU and OMC) of five individuals with no neurological disorders (control group) and five participants with iSCI walking for two minutes on a treadmill in a self-paced mode were analyzed. For validation purposes, the OMC system was considered as a reference. The mean absolute difference (MAD) between calculated range of motion of joint angles was , , , and for hip F/E, hip A/A, knee F/E, and ankle D/P flexion angles, respectively. In addition, relative stance, swing, double support phases, and cadence were calculated and validated. The MAD for the relative gait phases (stance, swing, and double support) was %, and the average cadence error was steps/min. The MAD values for RoM and relative gait phases can be considered as clinically acceptable. Therefore, we conclude that the proposed methodology is promising, enabling non-restrictive inertial gait analysis in clinical settings.

Assessment of the Impact of Using a Smart Thermostat and Smart Meter Data on a Whole-Building Energy Simulation

Building energy simulation models have been used to assist the design and/or optimization of buildings energy performance. The results from building energy simulation models can be more reliable when measured energy use data, indoor environmental condition data, system operation status, and coincident weather data are used to validate the simulation results. In this paper, given the wide-spread use of home automation devices in residential buildings, we studied how well a residential building energy simulation model can be tuned using measured interval data from a smart thermostat and smart meter. The analysis is based on a multi-stage approach that can help improve the reliability of the use of building energy simulation models that reflect both the indoor air temperature and whole-building energy use. Results from changing the input parameters in the building simulation show that the comparison of the simulated and measured indoor temperatures fall in a range below a NMBE of 1.5% and a CV-RMSE of 2.2%, while the simulated whole-building energy use matches the measured energy use below a NMBE of −2.7% and a CV-RMSE of 10.9%. We found that the most significant parameters for the indoor air temperature and whole-building energy use were the effective U-value for the slab-on-grade floor and the heating and cooling system operation status, respectively.

Prototyping an IoT-based system for monitoring building indoor environment

PurposeThis study aims to develop an architectural prototype of a Cyber-Physical System (CPS), as well as lay a technological foundation for future smart housing with improved health and well-being outcomes for its occupants.Design/methodology/approachThis study deploys smart sensors to monitor the key environmental parameters of a house. Using Internet of Things technology, a prototype of a CPS has been developed for capturing the environmental conditions over time. A case study involving a property in New Zealand was undertaken to validate the prototype.FindingsThe study proposes a monitoring platform, enabled by the CPS and smart sensing devices, that collects, shares, stores, analyses and visualises indoor environment data. The reliability and accuracy of the monitoring system were enhanced by comparing the activity of house occupants with sensor data.Research limitations/implicationsDue to limited time, the prototype was tested in one house for a period of one month. Air quality was not considered in this study. However, the work suggests that such an approach provides an effective solution for government organisations and housing agencies to collect information for the purpose of assessing building thermal performance.Originality/valueThis research proposes a new lens consisting of a home environment monitoring application with health and well-being implications. It could also be used to inform the future design of healthy homes and buildings, both in New Zealand and internationally.

Risk Assessment and Monitoring of Termites in the Forbidden City under Global Warming

ABSTRACT Historical heritage is facing enormous challenges as a result of global warming. Termites are a significant pest in ancient wooden buildings and their area of activity is also expanding. According to historical records, Reticulitermes chinensis Snyder and Reticulitermes speratus Kolbe are the main species of Reticulitermes present in Beijing. A 40-year record of meteorological data, termite damage information published by the Chinese government and indoor environmental data show that the risk of termite damage to the Forbidden City is increasing. In the first two decades of the twenty-first century, two termite infestations occurred in the Forbidden City. Monitoring and control systems were applied immediately, the risk to each object storeroom and other termite-susceptible area was classified and monitored, and the termites were controlled successfully. With the rapid development of information technology, an advanced termite monitoring system has been introduced that is based on non-closed-loop electromagnetic induction, is fully automatic and gives remote real-time monitoring. LoRa technology was used for signal transmission between the termite monitoring station and the information collection. This signal transmission technology can overcome the signal-blocking effect of the numerous walls inside the Forbidden City, ensuring that the signal can be transmitted quickly and accurately as soon as termite infestation is detected. The non-closed-loop design allows the electronic components to be fully encapsulated, improves resistance to soil moisture and salt erosion, prolongs service life and ensures accuracy. The risk assessment and remote real-time monitoring technology introduced in this paper are of great significance for protecting ancient buildings in the cities of northern China.

Real Estate Advisory Drone (READ): system for autonomous indoor space appraisals, based on Deep Learning and Visual Inertial Odometry

The present paper describes the development of a mobile platform as a support of the real estate appraisal procedure. Currently, the estate evaluation is performed by an expert that manually collects data, performs measurements, and grabs pictures of the inspected unit to finally evaluate its commercial value. The READ project aims at automatizing this process by developing a solution based on a mobile unit (drone or tablet) able to navigate the indoor environment and record data, which will be later processed on the cloud. To accomplish all these tasks, the platform is equipped with cameras, a LiDAR sensor, and a data process unit, with the goal of 1) understanding its motion and localization; 2) reconstructing a 3D map of the inspected space; 3) performing image-based analyses applying AI algorithms enabling the identification of the indoor space (e.g. bedroom or kitchen), the counting and the classification of furniture objects, and the detection of building imperfections or frauds. Tests have been performed in different scenarios providing promising results, laying the foundations for bringing these technologies into a real operational context.

A cost-effective, scalable, and portable IoT data infrastructure for indoor environment sensing

The vast number of facility management systems, home automation systems, and the ever-increasing number of Internet of Things (IoT) devices are in constant need of environmental monitoring. Indoor environment data can be utilized to improve indoor facilities and better occupants’ working and living experience, however, such data are scarce because many existing facility monitoring technologies are expensive and proprietary for certain building systems. With the aim of addressing the indoor environment data availability issue, the authors designed and prototyped a cost-effective, distributed, scalable, and portable indoor environmental data collection system, Building Data Lite (BDL). BDL is based on Raspberry Pi computers and multiple changeable arrays of sensors, such as sensors of temperature, humidity, light, motion, sound, vibration, and multiple types of gases. The system includes a distributed sensing network and a centralized server. The server provides a web-based graphical user interface that enables users to access the collected data over the Internet. To evaluate the BDL system’s functionality, cost effectiveness, scalability, and portability, the research team conducted a case study in an affordable housing community where the system prototype is deployed to 12 households. The results indicate that the system is functioning as designed, costs $73 per zone and provides 12 types of indoor environment data, is easy to scale up, and is fully portable. This research contributes to the body of knowledge by proposing an innovative way for establishing a distributed wireless IoT data infrastructure for indoor environment sensing in new or existing buildings.

Indoor Emergency Path Planning Based on the Q-Learning Optimization Algorithm

The internal structure of buildings is becoming increasingly complex. Providing a scientific and reasonable evacuation route for trapped persons in a complex indoor environment is important for reducing casualties and property losses. In emergency and disaster relief environments, indoor path planning has great uncertainty and higher safety requirements. Q-learning is a value-based reinforcement learning algorithm that can complete path planning tasks through autonomous learning without establishing mathematical models and environmental maps. Therefore, we propose an indoor emergency path planning method based on the Q-learning optimization algorithm. First, a grid environment model is established. The discount rate of the exploration factor is used to optimize the Q-learning algorithm, and the exploration factor in the ε-greedy strategy is dynamically adjusted before selecting random actions to accelerate the convergence of the Q-learning algorithm in a large-scale grid environment. An indoor emergency path planning experiment based on the Q-learning optimization algorithm was carried out using simulated data and real indoor environment data. The proposed Q-learning optimization algorithm basically converges after 500 iterative learning rounds, which is nearly 2000 rounds higher than the convergence rate of the Q-learning algorithm. The SASRA algorithm has no obvious convergence trend in 5000 iterations of learning. The results show that the proposed Q-learning optimization algorithm is superior to the SARSA algorithm and the classic Q-learning algorithm in terms of solving time and convergence speed when planning the shortest path in a grid environment. The convergence speed of the proposed Q- learning optimization algorithm is approximately five times faster than that of the classic Q- learning algorithm. The proposed Q-learning optimization algorithm in the grid environment can successfully plan the shortest path to avoid obstacle areas in a short time.

Estimating Indoor Population Density from Non-contact

With the spread of Covid-19, it is important to comprehend the indoor environment. It is also important to estimate indoor population density in order to avoid three Cs: Closed space, Crowded places, and Close-contact settings. This paper proposes a system for estimating indoor population density based on indoor environment data through Wireless Sensor Network (WSN). The proposed system collects indoor environment such as temperature, humidity, illuminace, CO2 concentration, and dust level. Then, the proposed system estimates indoor population density from collected environment data. The proposed system estimates the indoor population density from indoor environment data by machine learning. This study also investigates whether sensor data are adequate for estimating indoor population density. The experimental results show that the proposed system achieves about 80 % or more estimation accuracy by using multiple types of sensors, thereby demonstrating the effectiveness of the proposed system. The proposed system is expected to measure the closed space also estimate crowded rooms, which is a helpful finding for preventing the spread of COVID-19.

Using a calibrated multi-zone building model to analyse the impact of occupant behaviour on building performance

Building performance simulation has been commonly used for performance-based building design. However, the simulation accuracy is closely related to the model and input parameters regarding the building physics and occupant behaviour. To check and improve the accuracy of the simulation, the model usually needs to be calibrated using measured data. Uncertain parameters are adjusted in this process to reduce the discrepancy between simulation and measurement results. Although occupant behaviour has a significant impact on building performance, it has not been considered a key component of model calibration in previous studies. Model calibration using only indoor environmental and energy consumption data without considering occupant behaviour is unreliable. In this context, a step-by-step model calibration approach is proposed in this study to optimise the estimated parameters in typical operational scenarios where occupant behaviour can be conveniently determined. A case study shows that although the simulation results for apartment energy use are similar before and after model calibration, differences can be observed in the evaluation of indoor air quality and thermal environment. Simulation results using occupant behaviour based on design standards are significantly deviated from the measured values, compared to that using actual occupant behaviour.