Occupancy detection of residential buildings using smart meter data: A large-scale study

Razavi, Rouzbeh; Gharipour, Amin; Fleury, Martin; Akpan, Ikpe Justice

doi:10.1016/j.enbuild.2018.11.025

Cited by 94 publications

(33 citation statements)

References 30 publications

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“…Despite the intrusiveness of these systems being low, privacy can still be an important issue. The authors of [143] analysed the electricity consumption of more than 5000 households over a 18-month period and deployed several machine learning methods to forecast home occupancy in the short and long term. The results revealed that the present and future occupancy status of households can only be established with high confidence based on smart meter data.…”

Section: Use Of Nilm In Aalmentioning

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: Use Of Nilm In Aalmentioning

confidence: 99%

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

et al. 2019

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“…Fan et al [7] proposed methods for analyzing smart meter data and they have even used forecasting techniques to estimate the future energy consumption. Razavi et al [8] proposed a smart meter-based occupancy prediction method to estimate the number of occupants in a large building. The major challenge of the smart meter-based solution is that each building might have different energy needs, depending on the activities of the occupants.…”

Section: Occupancy Prediction In Indoor Environmentsmentioning

confidence: 99%

Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data

Arvidsson

Gullstrand

Sırmaçek

et al. 2021

Sensors

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Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and ventilation systems through considering the occupancy and activity information might reduce energy consumption by more than 50%. However, it is difficult to use high-resolution sensors and cameras for occupancy prediction due to privacy concerns. In this paper, we propose a novel solution for predicting occupancy using multiple low-cost and low-resolution heat sensors. We suggest two different methods for fusing and processing the data captured from multiple heat sensors and we use a Convolutional Neural Network for predicting occupancy. We conduct experiments to assess both the performance of the proposed solutions and analyze the impact of sensor field view overlaps on the prediction results. In summary, our experimental results show that the implemented solutions show high occupancy prediction accuracy and real-time processing capabilities.

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“…This great importance such systems on the improvement of human life has resulted in rapid adoption of smart indoor lighting in recent years [1][2][3][4][5]. The main components of a smart lighting system include dimmable light-emitting diodes (LEDs), photodetectors, occupancy sensors, and controller units; which have been the leading research topics over the recent years [6][7][8][9][10]. A communication protocol, which can be either wired or wireless, is employed for interconnecting the smart indoor lighting components [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Daylight adaptive smart indoor lighting control method using artificial neural networks

Seyedolhosseini

Masoumi

Modarressi

et al. 2020

Journal of Building Engineering

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Accurate and efficient adjustment of maintained illuminance and illuminance uniformity in indoor environments with daylight variations is a tremendous challenge, mainly due to the nonlinear and time-variant nature of lighting control systems. In this paper, we propose a smart lighting control method for indoor environments with both dimmable (controllable) and uncontrollable external light sources. Targeting an indoor environment with multiple zones, each requiring a different lighting condition and equipped with an unequal number of photodetectors and dimmable light sources, this paper presents a novel control mechanism that determines the output flux of each luminary in such a way that each zone (1) receives the required maintained illuminance, (2) illuminance uniformity conditions are met inside each zone, and (3) the power consumption is optimized. This method uses a neural network to learn the impact of each luminary on the maintained illuminance of each zone and adjust the dimming level of the luminaries to establish the required illuminance in the zones. We also rely on photodetectors to measure the daylight illuminance continuously and use it as the bias value for the neural network. The new priority value allows losing some illuminance accuracy (by allowing lager difference between the actual and required maintained illuminance values) for low-priority zones to reduce power consumption. The method has been evaluated in different test cases by chaining the widely-used DIALux tool and some MATLAB toolboxes. The evaluation results show that the method can achieve considerable accuracy by yielding an average Mean Square Error of 1.2 between the demanded and sensed illuminance values. Furthermore, when all sensors except one reference sensor are removed from each zone (to increase user comfort or reduce cost), the mean square error is less than 25.4 across all considered test cases.

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Occupancy detection of residential buildings using smart meter data: A large-scale study

Cited by 94 publications

References 30 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

Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data

Daylight adaptive smart indoor lighting control method using artificial neural networks

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