Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence

Jin, Ming; Jia, Ruoxi; Spanos, Costas J.

doi:10.1109/tmc.2017.2684806

Cited by 87 publications

(32 citation statements)

References 49 publications

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“…Other companies, such as ONZO Ltd. or Bidgely, Inc., propose similar approaches, most of them based on a smart meter/sensor and machine learning for energy disaggregation. With regard to the drawbacks presented by the commercial solutions, it is worth noting that most of them are constrained to low sampling rates, 1 Hz maximum [9,10], thus limiting the achieved performance and the chance to use them in some demanding types of applications. Even worse, sometimes this sampling frequency is not consistent over time, thus adding a new challenge.…”

Section: Data Collectionmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

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Section: Data Collectionmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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“…Nesa and Banerjee [34] have carried out the modeling of occupancy sensing where evidence theory has been used over the data aggregated by sensors. Jin et al [35] have worked on occupancy detection system using smart meters with the emphasis over the power management too. Similar trend of work is also carried out by Shen and Newsham [36] where occupancy detection is carried out by signals generated by Bluetooth signals of cellular devices.…”

Section: A the Backgroundmentioning

confidence: 99%

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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“…When they arrive at the office, they can rejoin the game. To enforce the rule that those who are not present in the space cannot vote remotely, we executed a simple presence detection algorithm based on their power usage [27], [28].…”

Section: A Social Game Experimental Set-upmentioning

confidence: 99%

A Robust Utility Learning Framework via Inverse Optimization

Konstantakopoulos

Ratliff

Jin

et al. 2018

IEEE Trans. Contr. Syst. Technol.

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Abstract-In many smart infrastructure applications, flexibility in achieving sustainability goals can be gained by engaging endusers. However, these users often have heterogeneous preferences that are unknown to the decision-maker tasked with improving operational efficiency. Modeling user interaction as a continuous game between non-cooperative players, we propose a robust parametric utility learning framework that employs constrained feasible generalized least squares estimation with heteroskedastic inference. To improve forecasting performance, we extend the robust utility learning scheme by employing bootstrapping with bagging, bumping, and gradient boosting ensemble methods. Moreover, we estimate the noise covariance which provides approximated correlations between players which we leverage to develop a novel correlated utility learning framework. We apply the proposed methods both to a toy example arising from Bertrand-Nash competition between two firms as well as to data from a social game experiment designed to encourage energy efficient behavior amongst smart building occupants. Using occupant voting data for shared resources such as lighting, we simulate the game defined by the estimated utility functions to demonstrate the performance of the proposed methods.

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Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence

Cited by 87 publications

References 49 publications

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

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

A Robust Utility Learning Framework via Inverse Optimization

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