Occupancy Detection Research Articles

Occupancy–detection models with museum specimen data: Promise and pitfalls

Abstract Historical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed to enable robust inferences. Occupancy–detection models, a relatively new and powerful suite of statistical methods, are a potentially promising avenue because they can account for changes in collection effort through space and time. We use simulated datasets to identify how and when patterns in data and/or modelling decisions can bias inference. We focus primarily on the consequences of contrasting methodological approaches for dealing with species' ranges and inferring species' non‐detections in both space and time. We find that not all datasets are suitable for occupancy–detection analysis but, under the right conditions (namely, datasets that are broken into more time periods for occupancy inference and that contain a high fraction of community‐wide collections, or collection events that focus on communities of organisms), models can accurately estimate trends. Finally, we present a case study on eastern North American odonates where we calculate long‐term trends of occupancy using our most robust workflow. These results indicate that occupancy–detection models are a suitable framework for some research cases and expand the suite of available tools for macroecological analysis available to researchers, especially where structured datasets are unavailable.

Deep learning and computer vision based occupancy CO2 level prediction for demand-controlled ventilation (DCV)

The present study investigated the potential of the application of a live occupancy detection approach to assist the operations of demand-controlled ventilation (DCV) systems to ensure that sufficient interior thermal conditions and air quality were attained while reducing unnecessary building energy loads to improve building energy performance. Faster region-based convolutional neural network (RCNN) models were trained to detect the number of people and occupancy activities respectively, and deployed to an artificial intelligence (AI)-powered camera. Experimental tests were carried out within a case study room to assess the performance of this approach. Due to the less complexity of people counting model, it achieved an average intersection over union (IoU) detection accuracy of about 98.9%, which was higher than activity detection model of about 88.5%. During the detection, the count-based occupancy profiles were produced according to the real-time information about the number of people and their activities. To estimate the effect of this approach on indoor air quality and energy demand, scenario-based modelling of the case study building under four ventilation scenarios was carried out via building energy simulation (BES). Results showed that the proposed approach could provide demand-driven ventilation controls data on the dynamic changes of occupancy to improve the indoor air quality (IAQ) and address the problem of under- or over-estimation of the ventilation demand when using the static or fixed profiles.

A novel CO2-based demand-controlled ventilation strategy to limit the spread of COVID-19 in the indoor environment

Ventilation is of critical importance to containing COVID-19 contagion in indoor environments. Keeping the ventilation rate at high level is recommended by many guidelines to dilute virus-laden respiratory particles and mitigate airborne transmission risk. However, high ventilation rate will cause high energy use. Demand-controlled ventilation is a promising technology option for controlling indoor air quality in an energy-efficient manner. This paper proposes a novel CO2-based demand-controlled ventilation strategy to limit the spread of COVID-19 in indoor environments. First, the quantitative relationship is established between COVID-19 infection risk and average CO2 level. Then, a sufficient condition is proposed to ensure COVID-19 event reproduction number is less than 1 under a conservative consideration of the number of infectors. Finally, a ventilation control scheme is designed to make sure the above condition can be satisfied. Case studies of different indoor environments have been conducted on a testbed of a real ventilation system to validate the effectiveness of the proposed strategy. Results show that the proposed strategy can efficiently maintain the reproduction number less than 1 to limit COVID-19 contagion while saving about 30%–50% of energy compared with the fixed ventilation scheme. The proposed strategy offers more practical values compared with existing studies: it is applicable to scenarios where there are multiple infectors, and the number of infectors varies with time; it only requires CO2 sensors and does not require occupancy detection sensors. Since CO2 sensors are very mature and low-cost, the proposed strategy is suitable for mass deployment in most existing ventilation systems.

Cost Effective Indoor HVAC Energy Efficiency Monitoring based on Intelligent Decision Support System under Fermatean Fuzzy Framework

The heating, ventilation, and air conditioning (HVAC) control system is responsible for the efficient building energy system. Indoor energy consumption patterns can be monitored and reduced intelligently. Occupancy information plays a vital role to save a reasonable amount of energy. Traditional energy monitoring and control systems can be improved with the installation of the occupancy monitoring system which will consist of a network of sensors and cameras. In this research work, we propose a new and revolutionary convolutional neural network (CNN) based on real-time camera occupancy detection and recognition techniques across different sorts of sensors that provide realistic low-cost energy-saving solutions with robust graphical processing units (GPUs). This occupancy information will decide the energy behaviour inside buildings. Decision-making tools can be used to select the appropriate occupancy detection and recognition alternative for indoor environment and energy monitoring and management. In this research work, we introduce and develop the "Fermatean fuzzy prioritized weighted average and geometric operator". These aggregation operators (AOs) are a modern approach to modelling complexities in decision-making. In the end, we give an algorithm for an intelligent decision support system (IDSS) using proposed AOs to compare our CNN based method with other existing sensors techniques.

Estimating Road Mortality Hotspots While Accounting for Imperfect Detection: A Case Study with Amphibians and Reptiles

Wildlife road mortality tends to aggregate spatially at locations commonly referred to as road mortality hotspots. Predictive models can be used to identify locations appropriate for mitigation measures that reduce road mortality. However, the influence of imperfect detection (e.g., false absences) during road mortality surveys can lead to inaccurate or imprecise spatial patterns of road mortality hotspots and suboptimal implementation of mitigation measures. In this research, we used amphibians and reptiles as a case study to address imperfect detection issues when estimating the probability of road mortality hotspots using occupancy detection modeling. In addition, we determined the survey effort needed to achieve a high probability of detecting large roadkill events. We also assessed whether vehicle travel reductions associated with the COVID-19 pandemic travel restrictions led to reductions in road mortality. We conducted surveys at 48 sites throughout Rhode Island, USA, from 2019–2021. In total, we observed 657 carcasses representing 19 of Rhode Island’s 37 native species. Of the 19 native species, eight species of frogs, four species of salamanders, four species of snakes, and three species of turtles were observed. We documented a reduction in roadkill density and the proportion of dead versus live amphibians and reptiles in pandemic years (2020 and 2021), but we were unable to link reductions in roadkill density to reductions in traffic volume. Our model results indicated that large roadkill events were more likely to occur on roads near wetlands and with low traffic volume and were more likely to be detected as daily precipitation increased. We determined that there was a low probability of detecting large roadkill events, suggesting that imperfect detection influences detection of large roadkill events, and many were likely missed during our surveys. Therefore, we recommend using occupancy modeling to account for the influence of imperfect detection when estimating road mortality hotspots. This approach will more effectively guide the implementation of mitigation measures.

Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context.

Studies and systems that are aimed at the identification of the presence of people within an indoor environment and the monitoring of their activities and flows have been receiving more attention in recent years, specifically since the beginning of the COVID-19 pandemic. This paper proposes an approach for people counting that is based on the use of cameras and Raspberry Pi platforms, together with an edge-based transfer learning framework that is enriched with specific image processing strategies, with the aim of this approach being adopted in different indoor environments without the need for tailored training phases. The system was deployed on a university campus, which was chosen as the case study. The proposed system was able to work in classrooms with different characteristics. This paper reports a proposed architecture that could make the system scalable and privacy compliant and the evaluation tests that were conducted in different types of classrooms, which demonstrate the feasibility of this approach. Overall, the system was able to count the number of people in classrooms with a maximum mean absolute error of 1.23.

Bayesian Workflow and Hidden Markov Energy-Signature Model for Measurement and Verification

A Bayesian data analysis workflow offers great advantages to the process of measurement and verification, including the estimation of savings uncertainty regardless of the chosen numerical model. However, it is still rarely used in practice, perhaps because practitioners are less familiar with the required tools. The present work documents a Bayesian methodology for the assessment of energy savings at the scale of a whole facility, following an energy-conservation measure. The first model, an energy signature commonly used in practice, demonstrates the steps of the Bayesian workflow and illustrates its advantages. The posterior distributions obtained by training this first model are used as prior distributions for a second, more complex model. This so-called “hidden Markov energy signature” model combines the energy signature with a hidden Markov model at an hourly resolution, and allows detection of occupancy. It has a large number of parameters and would likely not be identifiable without the Bayesian workflow. The results illustrate the advantages of the Bayesian methodology for measurement and verification: a probabilistic description of all variables, including predictions of energy use and savings; the applicability to any model structure; the ability to include prior knowledge to facilitate training complex models. Savings are estimated by the new hidden Markov energy-signature model with a much lower uncertainty than with a lower-resolution model. The highlights of the paper are twofold: it serves as a tutorial on Bayesian inference for measurement and verification; it also proposes a new flexible model structure for hourly prediction of energy use and occupancy detection.

Development and testing of a performance evaluation methodology to assess the reliability of occupancy sensor systems in residential buildings

With the emergence of advanced occupancy sensor technologies to better detect occupancy in buildings, a universal methodology and metrics are required to evaluate and report sensor systems’ reliability and compare the performance across multiple sensor systems. This research presents a methodology to assess the reliability of occupancy sensor systems in residential buildings in a controlled laboratory environment, including both “typical” and “failure” testing scenarios. The developed methodology was then implemented to evaluate a novel occupancy detection sensor system’s reliability. “Typical” testing evaluates the overall accuracy of the sensor system, which suggest how reliable the occupancy sensor system is over time. Results show that on average, the precision and recall are 0.75 and 0.70, indicating similar numbers of false positives and false negatives across the dataset. The overall accuracy of the tested sensor system was 62.4% to 76.4%. Failure testing results indicate whether there are influential variables impacting the sensor performance. For the tested sensor system, the number of occupants, presence of large objects, presence of interior light sources, and number of doors are not influential, while lighting level, location of occupants, additional door in the entry/exit area, and having the TV on are variables determined to impact the sensor system performance.

Car parking occupancy prediction

Nowadays in modern cities, with the continuous growth of cars, parking slot availability is becoming a more and more difficult task. So, an efficient car parking occupancy detection system is becoming a necessity in order to reduce traffic congestion in parking lots. This paper proposes a system based on computer vision algorithms and basic image processing techniques that is capable of determining if a parking space is occupied or not, using aerial images captured through a camera. It aims to solve the issue of detecting a parking space that minimizes the time spent in searching parking lots which in turn reduces the carbon emissions that lead to a better quality of life. Current solutions available to this problem use hardware devices, IoT, sensors, CNN, and various deep learning algorithms. But the proposed system eliminates all the above complexities, thus resulting in greatly decreased expenses.

The Information Gap in Occupant-Centric Building Operations: Lessons Learned from Interviews with Building Operators in Germany

Differences in building operator strategies can significantly affect building energy use and occupant comfort. However, it seems that the daily work of building operators and facility managers is still largely based on heuristics and individual experiences. In this work, we have investigated the current data collection methods during the operation and its daily use in buildings as well as the handling of occupant behavior, comfort, and user complaints based on interviews with ten building operators in Germany. These interviews were conducted as part of an international study of building operator OCC (Occupant-Centric Control) strategies, under the auspices of the IEA EBC Annex 79. The results of the interviews clearly reflect, that until now, communication between building operators and building occupants plays a more important role in optimizing or adjusting building operations to meet occupant needs than the data collected by BAS, which is mainly used to detect faults and check the system status of key HVAC components when faults occur. In some cases, the real-time data are applied for the adjustment of set points and schedules depending on measured conditions; however, customization of set points considering the user’s preferred temperature or ventilation rate or building operation based on occupancy detection has not yet been implemented in the considered buildings. The overall objective of this contribution to building operation research is to highlight best practices and identify white spaces that fulfill occupant requirements and achieve a high level of energy-efficiency. The presented findings identify current gaps between science and practice in the field of sustainable optimization of building operation, but also point out real-world starting points for future research and development.

An Occupancy-Informed Customized Price Design for Consumers: A Stackelberg Game Approach

Residential occupancy patterns are closely tied with consumers’ activities and can potentially be leveraged to improve demand response by identifying the periods when high demand occurs. Occupancy detection can be generally divided into two categories in existing studies: intrusive and non-intrusive detection. However, both methods are still challenging to implement in practice, due to privacy concerns raised by intrusive sensors and the lack of labeled data required by non-intrusive methods. This paper seeks to design customized prices based on different consumers’ occupancy status and consumption patterns to enhance both retailer and consumers’ benefits using smart meter data. The interaction between the retailer and consumers is modeled as a one-leader-N-follower Stackelberg game. A two-stage decision-making framework, including day-ahead and real-time, is proposed for revenue maximization. The ECO (Electricity Consumption and Occupancy) Swiss households dataset is adopted to evaluate the effectiveness of the proposed occupancy-informed demand response program design.

Multipath-Assisted Radio Sensing and State Detection for the Connected Aircraft Cabin.

Efficiency and reliable turnaround time are core features of modern aircraft transportation and key to its future sustainability. Given the connected aircraft cabin, the deployment of digitized and interconnected sensors, devices and passengers provides comprehensive state detection within the cabin. More specifically, passenger localization and occupancy detection can be monitored using location-aware communication systems, also known as wireless sensor networks. These multi-purpose communication systems serve a variety of capabilities, ranging from passenger convenience communication services, over crew member devices, to maintenance planning. In addition, radio-based sensing enables an efficient sensory basis for state monitoring; e.g., passive seat occupancy detection. Within the scope of the connected aircraft cabin, this article presents a multipath-assisted radio sensing (MARS) approach using the propagation information of transmitted signals, which are provided by the channel impulse response (CIR) of the wireless communication channel. By performing a geometrical mapping of the CIR, reflection sources are revealed, and the occupancy state can be derived. For this task, both probabilistic filtering and k-nearest neighbor classification are discussed. In order to evaluate the proposed methods, passenger occupancy detection and state detection for the future automation of passenger safety announcements and checks are addressed. Therefore, experimental measurements are performed using commercially available wideband communication devices, both in close to ideal conditions in an RF anechoic chamber and a cabin seat mockup. In both environments, a reliable radio sensing state detection was achieved. In conclusion, this paper provides a basis for the future integration of energy and spectrally efficient joint communication and sensing radio systems within the connected aircraft cabin.

CROOD: Estimating crude building occupancy from mobile device connections without ground-truth calibration

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.

Harvesting Ambient RF for Presence Detection Through Deep Learning.

This article explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using Wi-Fi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich information about the propagation environment. Through judicious preprocessing of the estimated CSI followed by deep learning, reliable presence detection can be achieved. Several challenges in passive RF sensing are addressed. With presence detection, how to collect training data with human presence can have a significant impact on the performance. This is in contrast to activity detection when a specific motion pattern is of interest. A second challenge is that RF signals are complex-valued. Handling complex-valued input in deep learning requires careful data representation and network architecture design. Finally, human presence affects CSI variation along multiple dimensions; such variation, however, is often masked by system impediments, such as timing or frequency offset. Addressing these challenges, the proposed learning system uses preprocessing to preserve human motion-induced channel variation while insulating against other impairments. A convolutional neural network (CNN) properly trained with both magnitude and phase information is then designed to achieve reliable presence detection. Extensive experiments are conducted. Using off-the-shelf Wi-Fi devices, the proposed deep-learning-based RF sensing achieves near-perfect presence detection during multiple extended periods of test and exhibits superior performance compared with leading edge passive infrared sensors. A comparison with existing RF-based human presence detection also demonstrates its robustness in performance, especially when deployed in a completely new environment. The learning-based passive RF sensing thus provides a viable and promising alternative for presence or occupancy detection.

Using Stream Data Processing for Real-Time Occupancy Detection in Smart Buildings.

Controlling active and passive systems in buildings with the aim of optimizing energy efficiency and maintaining occupants’ comfort is the major task of building management systems. However, most of these systems use a predefined configuration, which usually do not match the occupants’ preferences. Therefore, occupancy detection is imperative for energy use management mainly in residential and industrial buildings. Most works related to data-driven-based occupancy detection have used batch learning techniques, which need to store first and then train the data. It is not appropriate for a non-stationary environment. Therefore, this work sheds more light on the use of non-stationary machine learning techniques. To this end, three machine learning algorithms for stream data processing are presented, tested, and evaluated in term of accuracy and resources performance (i.e., RAM, CPU), with the aim of predicting the number of occupants in smart buildings. A platform architecture that integrates IoT technologies with stream machine learning is implemented and deployed. The experimental results show the effectiveness of this approach and illustrate that the number of occupants can be predicted with an accuracy of more than 83% and without resource wasting (i.e., time of CPU use varied between 0.04s and 3.85 ⋅ 10−11 GB of RAM could be exploited per hour).

OccupancySense: Context-based indoor occupancy detection & prediction using CatBoost model

Occupancy detection and prediction are two well-established problems which can be improved further to achieve higher accuracy in both cases than the existing solutions. To achieve the desired higher accuracy, proposed OccupancySense model detects human presence and predicts indoor occupancy count by the fusion of Internet of Things (IoT) based indoor air quality (IAQ) data along with static and dynamic context data which is a unique approach in this domain. This data fusion helps us to achieve higher forecasting accuracy along with the integration of state of the art gradient boosting based categorical features supported CatBoost algorithm. For comparison, other commonly used machine learning classification and regression algorithms, e.g., Multiple Linear Regression (MLR), Decision Tree (DT), Random Forests (RF) and Support Vector Machine (SVM) for regression and Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT) and Random Forest (RF), Support Vector Machine (SVM) for classification, were also assessed during this experiment. Out of these, CatBoost outperformed other models when considered in terms of accuracy. Hence, CatBoost is used as the core of the OccupancySense design and we have validated the proposed model by a real-world case study with continuous 91 days of indoor data, having 33 unique external features. These features are collected directly as well as derived from the collected data. To handle these features, feature engineering plays a key role in the OccupancySense model. The speciality of this model is, it is non-intrusive one but have high predictive power. It can detect occupancy and predicts headcount along with occupancy density of the room pretty accurately with 99.85%, 93.2% and 95.6% respectively (with 10 fold cross-validation) which outperforms other state of the art models.

Residential House Occupancy Detection: Trust-Based Scheme Using Economic and Privacy-Aware Sensors

Internet of Things (IoT) technologies (e.g., power-efficient occupancy-based energy management systems) are increasingly deployed in commercial buildings to reduce building energy consumption. However, the sensors involved in such systems are rarely adopted in residential houses due to their relatively high costs and users’ privacy concerns. Low-cost and nonintrusive IoT sensors have been proposed for residential houses for use with machine-learning algorithms. Furthermore, such sensors may be triggered very infrequently due to their nonintrusive nature, and it can take several days/weeks to collect sufficient training data. There is a research gap in accurately detecting occupancy information in residential houses with limited training data. This article proposes a trust-based occupancy detection scheme, which achieves high detection accuracy based on limited training data collected by nonintrusive, low-cost sensors. First, rather than directly taking raw sensor data as inputs, the semantic meanings (i.e., human activity sequences) are extracted from the data based on the order of triggered sensors. Second, the extracted human activity sequences are fed into the proposed trust-based sequence matching scheme for further occupancy detection. Comprehensive experimental results show that, when compared to existing occupancy detection algorithms, the proposed scheme can reliably achieve higher accuracy, especially when only limited training data is available.

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

• Review of applications of transfer learning (TL) for smart buildings. • Identification of main application areas of TL in smart buildings. • Insights on the most-effective TL techniques for each application area. • Discussion on current research gaps and future opportunities. Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building’s ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions.

Using RF Transmissions From IoT Devices for Occupancy Detection and Activity Recognition

Using RF Transmissions From IoT Devices for Occupancy Detection and Activity Recognition

The COVID-19 impact on air condition usage: a shift towards residential energy saving.

The enforcement of the Movement Control Order to curtail the spread of COVID-19 has affected home energy consumption, especially HVAC systems. Occupancy detection and estimation have been recognized as key contributors to improving building energy efficiency. Several solutions have been proposed for the past decade to improve the precision performance of occupancy detection and estimation in the building. Environmental sensing is one of the practical solutions to detect and estimate occupants in the building during uncertain behavior. However, the literature reveals that the performance of environmental sensing is relatively poor due to the poor quality of the training dataset used in the model. This study proposed a smart sensing framework that combined camera-based and environmental sensing approaches using supervised learning to gather standard and robust datasets related to indoor occupancy that can be used for cross-validation of different machine learning algorithms in formal research. The proposed solution is tested in the living room with a prototype system integrated with various sensors using a random forest regressor, although other techniques could be easily integrated within the proposed framework. The primary implication of this study is to predict the room occupation through the use of sensors providing inputs into a model to lower energy consumption. The results indicate that the proposed solution can obtain data, process, and predict occupant presence and number with 99.3% accuracy. Additionally, to demonstrate the impact of occupant number in energy saving, one room with two zones is modeled each zone with air condition with different thermostat controller. The first zone uses IoFClime and the second zone uses modified IoFClime using a design-builder. The simulation is conducted using EnergyPlus software with the random simulation of 10 occupants and local climate data under three scenarios. The Fanger model’s thermal comfort analysis shows that up to 50% and 25% energy can be saved under the first and third scenarios.