Building Occupancy Detection Research Articles

Deep Weighted Fusion Learning (DWFL)-based multi-sensor fusion model for accurate building occupancy detection

With the advancement of artificial intelligence, the dominance of deep learning (DL) models over ordinary machine learning (ML) algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition. With the growing demands for power savings, building energy loss reduction could benefit from DL techniques. For buildings/rooms with the varying number of occupants, heating, ventilation, and air conditioning (HVAC) systems are often found in operations without much necessity. To reduce the building’s energy loss, accurate occupancy detection/prediction (ODP) results could be used to control the proper operations of HVACs. However, ODP is a challenging issue due to multiple reasons, such as improper selection/deployment of sensors, inefficient learning algorithms for pattern recognition, varying room conditions, etc. To overcome the above challenges, we propose a DL-based framework, i.e., Deep Weighted Fusion Learning (DWFL), to detect and predict occupancy counts with optimal multi-sensor fusion structure. DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor. Such weight assignment considers different room conditions and the pros/cons of each type of sensor. To evaluate DWFL model in terms of occupancy prediction accuracy, we have set up an experimental testbed with low-cost cameras, carbon dioxide (CO2), and passive infrared (PIR) sensors. Among the recently proposed occupancy detection models, DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88% accuracy in occupancy count estimation (Xue et al., 2019). Another deep learning-based model MI-PIR achieved 91% accuracy on raw analog data from PIR sensors (Andrews et al., 2020). Our research outcome is 94%. Therefore, the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.

Multimodal Framework for Smart Building Occupancy Detection

Over the years, building appliances have become the major energy consumers to improve indoor air quality and occupants’ lifestyles. The primary energy usage in building sectors, particularly lighting, Heating, Ventilation, and Air conditioning (HVAC) equipment, is expected to double in the upcoming years due to inappropriate control operation activities. Recently, several researchers have provided an automated solution to turn HVAC and lighting on when the space is being occupied and off when the space becomes vacant. Previous studies indicate a lack of publicly accessible datasets for environmental sensing and suggest developing holistic models that detect buildings’ occupancy. Additionally, the reliability of their solutions tends to decrease as the occupancy grows in a building. Therefore, this study proposed a machine learning-based framework for smart building occupancy detection that considered the lighting parameter in addition to the HVAC parameter used in the existing studies. We employed a parametric classifier to ensure a strong correlation between the predicting parameters and the occupancy prediction model. This study uses a machine learning model that combines direct and environmental sensing techniques to obtain high-quality training data. The analysis of the experimental results shows high accuracy, precision, recall, and F1-score of the applied RF model (0.86, 0.99, 1.0, and 0.88 respectively) for occupancy prediction and substantial energy saving.

Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues

Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues

DMFF: Deep multimodel feature fusion for building occupancy detection

The 2022 Global Status Report for Buildings and Construction (Buildings-GSR) indicates that construction activities have returned to pre-pandemic levels in most major economies, alongside more building energy consumption. To achieve the Net Zero emissions target by 2050, particularly in the post-pandemic era, accurate occupancy information is important to enhance building energy efficiency and improve occupancy comfort. While remarkable progress has been made in existing studies, they struggle to make full use of multi-sensor data to achieve high accuracy. Furthermore, there is a high expectation of the multimodel multi-temporary fusion by Transformer. In this study, we present a Transformer-based multimodal, multi-temporal feature fusion method (DMFF) for occupancy detection. To transfer domain knowledge from artificial intelligence into the building area, DMFF includes a pretrain-finetune pipeline and leverages pre-trained visual and sound models. Multiple pre-trained Transformer encoders are employed to extract features of different modalities. Then, we propose a self-attention mechanism for modality fusion to learn relationships among various sensors. Our DMFF method demonstrates superior performance on a real dataset, outperforming machine and deep learning methods (e.g., Convolutional Neural Networks, Random Forest, and Multilayer Perceptrons). Applied to a room setting, DMFF shows promising potential for building energy savings. The code and demo are accessible at https://github.com/kailaisun/multimodel_occupancy.

A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring

The study deals with detection of the occupation of Intelligent Building (IB) using data obtained from indirect methods with Big Data Analysis within IoT. In the area of daily living activity monitoring, one of the most challenging tasks is occupancy prediction, giving us information about people's mobility in the building. This task can be done via monitoring of CO2 as a reliable method, which has the ambition to predict the presence of the people in specific areas. In this paper, we propose a novel hybrid system, which is based on the Support Vector Machine (SVM) prediction of the CO2 waveform with the use of sensors that measure indoor/outdoor temperature and relative humidity. For each such prediction, we also record the gold standard CO2 signal to objectively compare and evaluate the quality of the proposed system. Unfortunately, this prediction is often linked with a presence of predicted signal activities in the form of glitches, often having an oscillating character, which inaccurately approximates the real CO2 signals. Thus, the difference between the gold standard and the prediction results from SVM is increasing. Therefore, we employed as the second part of the proposed system a smoothing procedure based on Wavelet transformation, which has ambitions to reduce inaccuracies in predicted signal via smoothing and increase the accuracy of the whole prediction system. The whole system is completed with an optimization procedure based on the Artificial Bee Colony (ABC) algorithm, which finally classifies the wavelet's response to recommend the most suitable wavelet settings to be used for data smoothing.

Building Occupancy Detection and Localization Using CCTV Camera and Deep Learning

Occupancy information plays a key role in analyzing and improving building energy performance. The advances of Internet of Things (IoT) technologies have engendered a shift in measuring building occupancy with IoT sensors, in which cameras in closed-circuit television (CCTV) systems can provide richer measurements. However, existing camera-based occupancy detection approaches cannot function well when scanning videos with a number of occupants and determining occupants’ locations. This article aims to develop a novel deep-learning-based approach for better building occupancy detection based on CCTV cameras. To do so, this research proposes a deep-learning model to detect the number of occupants and determine their locations in videos. This model consists of two main modules, namely, feature extraction and three-stage occupancy detection. The first module presents a deep convolutional neural network to perform residual and multibranch convolutional calculation to extract shallow and semantic features, and constructs feature pyramids through a bidirectional feature network. The second module performs a three-stage detection procedure with three sequential and homogeneous detectors which have increasing Intersection over Union (IoU) thresholds. Empirical experiments evaluate the detection performance of the approach with CCTV videos from a university building. Experimental results show that the approach achieves the superior detection performance when compared with baseline models.

Deep and transfer learning for building occupancy detection: A review and comparative analysis

The building internet of things (BIoT) is quite a promising concept for curtailing energy consumption, reducing costs, and promoting building transformation. Besides, integrating artificial intelligence (AI) into the BIoT is essential for data analysis and intelligent decision-making. Thus, data-driven approaches to infer occupancy patterns usage are gaining growing interest in BIoT applications. Typically, analyzing big occupancy data gathered by BIoT networks helps significantly identify the causes of wasted energy and recommend corrective actions. Within this context, building occupancy data aids in the improvement of the efficacy of energy management systems, allowing the reduction of energy consumption while maintaining occupant comfort. Occupancy data might be collected using a variety of devices. Among those devices are optical/thermal cameras, smart meters, environmental sensors such as carbon dioxide (CO2), and passive infrared (PIR). Even though the latter methods are less precise, they have generated considerable attention owing to their inexpensive cost and low invasive nature. This article provides an in-depth survey of the strategies used to analyze sensor data and determine occupancy. The article’s primary emphasis is on reviewing deep learning (DL), and transfer learning (TL) approaches for occupancy detection. This work investigates occupancy detection methods to develop an efficient system for processing sensor data while providing accurate occupancy information. Moreover, the paper conducted a comparative study of the readily available algorithms for occupancy detection to determine the optimal method in regards to training time and testing accuracy. The main concerns affecting the current occupancy detection system in terms of privacy and precision were thoroughly discussed. For occupancy detection, several directions were provided to avoid or reduce privacy problems by employing forthcoming technologies such as edge devices, Federated learning, and Blockchain-based IoT.

MPSN: Motion-aware Pseudo-Siamese Network for indoor video head detection in buildings

Indoor video head detection is an essential component of building occupancy detection. While deep learning models have achieved remarkable progress in general object detection, their performance is limited in complex indoor scenes. Indoor surveillance videos often contain cluttered background objects, among which heads have small scales and diverse poses. In this paper, we propose Motion-aware Pseudo-Siamese Network (MPSN), an end-to-end approach that leverages head motion information to guide deep learning models to extract effective head features in indoor scenarios. By taking the pixel-wise difference between adjacent frames as the auxiliary input, MPSN effectively enhances human head motion information and removes irrelevant background objects. Its performance is compared with that of existing methods. Compared with existing methods, MPSN achieves superior performance on two indoor video datasets. It successfully suppresses static background objects and highlights the moving instances, especially human heads in indoor videos. Different methods to capture head motion also were compared. MPSN demonstrates simplicity and flexibility compared to other methods. We validate its robustness through adversarial experiments with a mathematical solution of small perturbations for robust model selection. To confirm its potential in building control systems, we apply MPSN to occupancy counting. The code is available at https://github.com/pl-share/MPSN.

Multimodal sensor fusion framework for residential building occupancy detection

For several years now, smart building energy systems have been a research area of intensive activity. In light of the increasing need for sustainable buildings and energy systems, this trend motivates an increasing need for a solution to reduce carbon dioxide emissions and improve energy efficiency. This paper proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. To draw out the best prediction performance in each modality, the proposed framework was developed, including various models that were designed to learn the occupancy patterns reflected in the physical data streams. To tackle the time series environmental data, we designed two variants of an occupancy detection spatiotemporal pattern network (Occ-STPN) that performs both feature level and decision level fusion, respectively. We also propose a new metric; the fading memory mean square error (FMMSE), that provides a fair evaluation and penalization of delayed occupancy predictions. Multiple open-sourced datasets, including the Electricity Consumption and Occupancy and the University of California, Irvine’s (UCI) building occupancy detection dataset, along with our own real data collected from six different houses, were used to validate the algorithms’ performance. The experimental results presented herein break down the performance for each sensing modality, and a detailed analysis of the performance is also discussed.

A high-fidelity residential building occupancy detection dataset

This paper describes development of a data acquisition system used to capture a range of occupancy related modalities from single-family residences, along with the dataset that was generated. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally identifiable information; indoor environmental readings, captured every ten seconds; and ground truth binary occupancy status. The data acquisition system, coined the mobile human presence detection (HPDmobile) system, was deployed in six homes for a minimum duration of one month each, and captured all modalities from at least four different locations concurrently inside each home. The environmental modalities are available as captured, but to preserve the privacy and identity of the occupants, images were downsized and audio files went through a series of processing steps, as described in this paper. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality.

Demonstrating the potential of indoor positioning for monitoring building occupancy through ecologically valid trials

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

Development of CNN-based visual recognition air conditioner for smart buildings

Demand-driven heating, ventilation, and air conditioning (HVAC) operations have become very attractive in energy-efficient smart buildings. Demand-oriented HVAC control largely relies on accurate detection of building occupancy levels and locations. So far, existing building occupancy detection methods have their disadvantages, and cannot fully meet the expected performance. To address this challenge, this paper proposes a visual recognition method based on convolutional neural networks (CNN), which can intelligently interpret visual contents of surveillance cameras to identify the number of occupants and their locations in buildings. The proposed study can detect the quantity, distance, and angle of indoor human users, which is essential for controlling air-conditioners to adjust the direction and speed of air blow. Compared with the state of the art, the proposed method successfully fulfills the function of building occupant counting, which cannot be realized when using PIR, sound, and carbon dioxide sensors. Our method also achieves higher accuracy in detecting moving or stationary human bodies and can filter out false detections (such as animal pets or moving curtains) that are existed in previous solutions. The proposed idea has been implemented and collaboratively tested with air conditioners in an office environment. The experimental results verify the validity and benefits of our proposed idea.

Cascade neural network algorithm with analytical connection weights determination for modelling operations and energy applications

The performance and learning speed of the Cascade Correlation neural network (CasCor) may not be optimal because of redundant hidden units’ in the cascade architecture and the tuning of connection weights. This study explores the limitations of CasCor and its variants and proposes a novel constructive neural network (CNN). The basic idea is to compute the input connection weights by generating linearly independent hidden units from the orthogonal linear transformation, and the output connection weights by connecting hidden units in a linear relationship to the output units. The work is unique in that few attempts have been made to analytically determine the connection weights on both sides of the network. Experimental work on real energy application problems such as predicting powerplant electrical energy, predicting seismic hazards to prevent fatal accidents and reducing energy consumption by predicting building occupancy detection shows that analytically calculating the connection weights and generating non-redundant hidden units improves the convergence of the network. The proposed CNN is compared with that of the state-of-the-art machine learning algorithms. The work demonstrates that proposed CNN predicts a wide range of applications better than other methods.

BLEMAT: Data Analytics and Machine Learning for Smart Building Occupancy Detection and Prediction

Running costs of buildings represent a significant outlay for all businesses, thus finding a way to run facilities as efficiently as possible is vital. IoT-enabled Building Management Systems provide means for process and resource usage automation leading to overall efficiency improvements. Inferring spatial and temporal occupancy in all its forms (binary, numerical or continuous) is one of the key contextual inputs required for smart building management systems. In this work, we showcase design, implementation and experimental validation of a smart building occupancy detection and forecasting solution. The presented solution comprises three main building blocks: (1) A fog computing indoor positioning system (BLEMAT — Bluetooth Low Energy Microlocation Asset Tracking) which, combined with wireless access network monitoring processes, produces indoor location information in a semi-unsupervised manner; (2) Data analysis and pattern searching pipelines responsible for fusing data coming from different smart building and networking systems and deriving information on temporal and spatial occupancy patterns; (3) Long short-term memory (LSTM) neural networks trained to predict occupancy patterns in different areas of a smart building. Data analysis and neural network training are conducted on real-world smart building dataset which authors provide in public online repository. Experimental validation confirms that the proposed solution can provide actionable occupancy detection and prediction information, required by smart building management systems.

Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering

Fusing various sensing data sources can significantly improve the accuracy and reliability of building occupancy detection. Fusing environmental sensors and wireless network signals are seldom studied for its computational and technical complexity. This study aims to propose an integrated adaptive lasso model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources. Through rapid feature extraction and process simplification, the proposed method aims to improve the computational efficiency of occupancy detecting models. To validate the proposed model, an onsite experiment was conducted to examine two occupancy data resolutions, (real-time and four-level occupancy resolutions). The results suggested that, among all twelve features, eight features are most relevant. The mean absolute error of the real-time occupancy can be reduced to 2.18 and F1_accuracy is about 84.36% for the four-level occupancy.

Building Occupancy Detection for Intelligent HVAC Management

Building Occupancy Detection for Intelligent HVAC Management

Design of Building Heating Supply Energy-Saving Control System Based on Information Fusion

An energy-saving control system for building heating supply system is presented in this paper. Its design and implementation are on the basis of occupancy detection in buildings. By taking advantage of the information fusion technology, the control system will determine human’s location and control the heating equipments automatically according to non-human zone and human zone to realize the energy saving. The information fusion technology fuses three categories of information, which are the people counting information, the infrared sensors information and the RFID information. How to design and implement the heating energy-saving control system is described explicitly in this paper. Since the system has developed with platform concept based on the wireless network and the Internet, it can be used to other building electrical equipment systems with less modification.

Building occupancy detection through sensor belief networks

Currently it is difficult to determine when and where people occupy a commercial building. Part of the difficulty arises from shortcomings in available sensor technology, but an even greater deficiency is the lack of analysis methods appropriate to the determination of occupancy. This paper describes a pilot study describing new sensing and data analysis techniques, applied to the determination of space occupancy. The central premise of the paper is that improved building operation with respect to energy management, security, and indoor environmental quality will be possible with better detection of building occupancy resolved in space and time. We developed and deployed a network of passive infrared occupancy sensors in two private offices, and applied analysis tools based on Bayesian probability theory to determine occupancy. Specifically, a class of graphical probability models, called belief networks, was applied to the occupancy data generated by the sensor network. The inference of primary importance is a probability distribution over the number of occupants and their locations in a building, given past and present sensor measurements. Inferences were computed for occupancy and its temporal persistence in individual offices as well as the persistence of sensor status. The raw sensor data were also used to calibrate the sensor belief network, including the occupancy transition matrix used in the Markov model, sensor sensitivity, and sensor failure models. This study shows that the belief network framework can be applied to the analysis of data streams from sensor networks, offering significant benefits to building operation compared to current practice.