Estimating Occupancy from Measurements and Knowledge Using the Bayesian Network for Energy Management

Amayri, Manar; Ploix, Stéphane; Kazmi, Hussain; Ngo, Quoc-Dung; Safadi, E. L. Abed E. L.

doi:10.1155/2019/7129872

Cited by 22 publications

(14 citation statements)

References 17 publications

(21 reference statements)

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“…Adoptiion of inductive principle for similar purpose was also seen in work of George et al [25]. Most recently, the work of Amayri et al [26] have presented a model that uses Bayesian Network for estimating occupancy. The performance of the occupancy sensing is further investigated with respect to pattern-based approach along with the compressive sensing over under-used spectrum as seen in the work of Eltabie et al [27].…”

Section: A the Backgroundmentioning

confidence: 84%

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

K.Mane*,

Rao

2019

IJITEE

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Occupancy sensing is one of predominant technology used in various control and context aware systems. The efficiency of such systems is highly corelated with the level of accuracy of the classification model of activity or stage detection of the subject or an occupant. The classification approaches based on computer need to evolve optimally for balancing the computational and time complexity with the classication accuracy using occupancy data acquired from Doppler radar. The proposed study presents a simplified framework that emphasizes on feature extraction technique as a medium to obtain more precision in the process of monitoring occupancy. The proposed logic has been implemented using analytical methodology convention and the extracted feature has been subjected to different forms of frequently used machine learning process with respect to processing time inclusive of training and testing period, efficiency, and accuracy of the proposed system.

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Section: A the Backgroundmentioning

confidence: 84%

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

K.Mane*,

Rao

2019

IJITEE

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“…Regarding the number of sensors deployed in the enclosed space, there are researchers who have deployed around 240 sensors in an office and achieved 91% accuracy [ 65 ]. In contrast, there are other researchers who have installed a single sensor in an office with an area of 186 m

, obtaining 94% accuracy [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Mena

Ceballos

Alvarado-Uribe

2022

Sensors

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The COVID-19 pandemic has changed our common habits and lifestyle. Occupancy information is valued more now due to the restrictions put in place to reduce the spread of the virus. Over the years, several authors have developed methods and algorithms to detect/estimate occupancy in enclosed spaces. Similarly, different types of sensors have been installed in the places to allow this measurement. However, new researchers and practitioners often find it difficult to estimate the number of sensors to collect the data, the time needed to sense, and technical information related to sensor deployment. Therefore, this systematic review provides an overview of the type of environmental sensors used to detect/estimate occupancy, the places that have been selected to carry out experiments, details about the placement of the sensors, characteristics of datasets, and models/algorithms developed. Furthermore, with the information extracted from three selected studies, a technique to calculate the number of environmental sensors to be deployed is proposed.

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“…The first method applies one sensor or sensor fusion to evaluate the presence and actions of occupants as well as the indoor environmental quality. On the other hand, the main drawbacks of this method are represented by privacy, cost, accuracy, sample size, location availability, and social acceptability concerns (Amayri et al, 2019;Ahmad et al, 2020). Questionnaire surveys and interviews aim at collecting quantitative results from a population sample regarding the lifestyles of occupants.…”

Section: Occupancy Detection Modelsmentioning

confidence: 99%

Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms

Elshaboury¹

2022

10.5267/j.dsl

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There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.

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Estimating Occupancy from Measurements and Knowledge Using the Bayesian Network for Energy Management

Cited by 22 publications

References 17 publications

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms

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