Estimating occupancy in heterogeneous sensor environment

Amayri, Manar; Arora, Abhay; Ploix, Stéphane; Bandhyopadyay, Sanghamitra; Ngo, Quoc-Dung; Badarla, Venkata Ramana

doi:10.1016/j.enbuild.2016.07.026

Cited by 109 publications

(33 citation statements)

References 15 publications

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“…For the selection method of key variables, the entropy-based information theory method, which evaluates the impurity of output values within a dataset, was introduced. The information theory method has been used in recent studies for a similar purpose (Dong et al [14]; Zhang et al [32]; Ekwevugbe et al [37]; Yang et al [18]; Arora et al [25]; Amayri et al [41]; Ryu and Moon [42]; Masood et al [43]). In this study, the gain ratio, one of the information theory methods, was used.…”

Section: Selection Of Key Input Variablesmentioning

confidence: 99%

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

et al. 2019

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To improve the energy prediction performance of a building energy model, the occupancy status information is very important. This is more important in real buildings, rather than under construction buildings, because actual building occupancy can significantly influence its energy consumption. In this study, a machine learning based framework for a consecutive occupancy estimation is proposed by utilizing internet of things data, such as indoor temperature and luminance, CO2 density, electricity consumption of lighting, HVAC (heating, ventilation, and air conditioning), electric appliances, etc. Three machine learning based occupancy estimation algorithms (decision tree, support vector machine, artificial neural networks) are selected and evaluated in terms of the performance of estimating the occupancy status for each season. The selection process of the input variables that have crucial impact on the algorithms’ performance are described in detail. Finally, an occupancy estimation framework that can repeat model training and estimation consecutively in a situation when time-series data are continuously provided over the entire measurement period is suggested. In addition, the performance of the framework is evaluated to identify how it improves the energy prediction performance of the building energy model compared to conventional energy modeling practices. The suggested framework is distinguished from similar previous studies in two ways: 1) The proposed framework reveals that input variables for the occupancy estimation model can be occasionally changed by an occupant response to certain times and seasons, and 2) the framework incorporates time-series indirect occupancy sensing data and classification algorithms to consecutively provide occupancy information for the energy modeling effort.

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Section: Selection Of Key Input Variablesmentioning

confidence: 99%

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

et al. 2019

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“…These studies usually require high-performance devices or cameras (leading to privacy concerns) to make accurate calculations due to the requirements on the outdoor environments. Indoor examples for this task include using various videos/images such as from a monocular camera on top of a door [17], multiple cameras in smart environments [18,19], infrared and ultrasonic sensors [20,21], Wi-Fi signals [22][23][24][25], RFID [26], structural vibrational sensing [27], CO 2 sensors, and microphones [28]. All these methods might provide good results depending on the environment and fine-tuning, but sacrificing security/privacy of users (camera-based and Wi-Fi based solutions), having high computation overhead (camera-based solutions), or having low accuracy due to low data quality (ultrasonic-, infrared-, and RFID-based solutions, among others).…”

Section: Number Of People Estimationmentioning

confidence: 99%

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Singh

Akşanlı

2019

JSAN

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This paper presents a framework to accurately and non-intrusively detect the number of people in an environment and track their positions. Different from most of the previous studies, our system setup uses only ambient thermal sensors with low-resolution, using no multimedia resources or wearable sensors. This preserves user privacy in the environment, and requires no active participation by the users, causing no discomfort. We first develop multiple methods to estimate the number of people in the environment. Our experiments demonstrate that algorithm selection is very important, but with careful selection, we can obtain up to 100% accuracy when detecting user presence. In addition, we prove that sensor placement plays a crucial role in the system performance, where placing the sensor on the room ceiling yields to the best results. After accurately finding the number of people in the environment, we perform position tracking on the collected ambient data, which are thermal images of the space where there are multiple people. We consider position tracking as static activity detection, where the user's position does not change while performing activities, such as sitting, standing, etc. We perform efficient pre-processing on the data, including normalization and resizing, and then feed the data into well-known machine learning methods. We tested the efficiency of our framework (including the hardware and software setup) by detecting four static activities. Our results show that we can achieved up to 97.5% accuracy when detecting these static activities, with up to 100% class-wise precision and recall rates. Our framework can be very beneficial to several applications such as health-care, surveillance, and home automation, without causing any discomfort or privacy issues for the users.

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“…The outcome consists of offline policies to optimise energy usage across the campus. In [13] a different application is described using decision trees for occupancy estimation in office buildings. Occupancy modelling and estimation is a critical task in smart buildings as the occupancy level and its accurate forecasting directly impact the HVAC conditioning strategy of the building and avoiding wasteful control.…”

Section: B Related Workmentioning

confidence: 99%

Data-Driven Modelling of Smart Building Ventilation Subsystem

et al. 2019

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Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations the application of conventional system identification or approximate greybox models, partly theoretic and partly data-driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.

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Estimating occupancy in heterogeneous sensor environment

Cited by 109 publications

References 15 publications

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Data-Driven Modelling of Smart Building Ventilation Subsystem

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