Occupancy Detection from Electricity Consumption Data

Kleiminger, Wilhelm; Beckel, Christian; Staake, Thorsten; Santini, Silvia

doi:10.1145/2528282.2528295

Cited by 196 publications

(129 citation statements)

References 22 publications

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“…In a recent workshop publication we presented the results of a preliminary analysis of an occupancy monitoring infrastructure relying on electricity consumption data [18]. At the same workshop, Chen et al discussed the potential of digital electricity meters to be used for performing non-intrusive occupancy monitoring [6].…”

Section: Occupancy and The Electric Load Curvementioning

confidence: 99%

“…In our previous work [18], we report a preliminary analysis of this data set and show that digital electricity meters are suitable to be used as occupancy sensors. In this paper, we build and improve upon our previous work and present a detailed analysis of supervised machine learning approaches to detect occupancy from electricity consumption data.…”

Section: Introductionmentioning

confidence: 99%

“…The fact that electricity consumption measurements might indicate the presence of residents in a household is intuitive and has already been observed earlier [18,23]. However there does not yet exist a comprehensive, quantitative analysis of the accuracy achievable using electricity meters to detect household occupancy.…”

Section: Introductionmentioning

confidence: 99%

“…• Exhaustive analysis of the feature space: To investigate which characteristics of the load curve best reveal occupancy, we extend the feature set of our preliminary work [18] from 10 to 35 features. To deal with the extended feature set, we employ dimensionality reduction, in particular principal component analysis (PCA) and feature selection.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Household occupancy monitoring using electricity meters

Kleiminger

Beckel

Santini

2015

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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112

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Occupancy monitoring (i.e. sensing whether a building or room is currently occupied) is required by many building automation systems. An automatic heating system may, for example, use occupancy data to regulate the indoor temperature. Occupancy data is often obtained through dedicated hardware such as passive infrared sensors and magnetic reed switches. In this paper, we derive occupancy information from electric load curves measured by off-the-shelf smart electricity meters. Using the publicly available ECO dataset, we show that supervised machine learning algorithms can extract occupancy information with an accuracy between 83% and 94%. To this end we use a comprehensive feature set containing 35 features. Thereby we found that the inclusion of features that capture changes in the activation state of appliances provides the best occupancy detection accuracy.

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Section: Occupancy and The Electric Load Curvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Household occupancy monitoring using electricity meters

Kleiminger

Beckel

Santini

2015

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Self Cite

112

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show abstract

“…Recently, many authors proposed to analyse data from multiple sensors (electric power meters, accelerometers and noise meters) to identify the user's presence or absence in a space [4,5]: variations of sensor time series, due to the arrival or exit of a user from a space, are analyzed to infer a classification template for the user's presence/absence. Conversely, refs.…”

Section: Introductionmentioning

confidence: 99%

Computational Topology to Monitor Human Occupancy

Barsocchi

Cassarà

Giorgi

et al. 2018

International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

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Abstract:The recent advances in sensing technologies, embedded systems, and wireless communication technologies, make it possible to develop smart systems to monitor human activities continuously. The occupancy of specific areas or rooms in a smart building is an important piece of information, to infer the behavior of people, or to trigger an advanced surveillance module. We propose a method based on computational topology to infer the occupancy of a room monitored for a week by a system of low-cost sensors.

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A platform architecture for occupancy detection using stream processing and machine learning approaches

Elkhoukhi

NaitMalek

Bakhouya

et al. 2019

Concurrency and Computation

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SummaryContext‐awareness in energy‐efficient buildings has been considered as a crucial fact for developing context‐driven control approaches in which sensing and actuation tasks are performed according to the contextual changes. This could be done by including the presence of occupants, number, actions, and behaviors in up‐to‐date context, taking into account the complex interlinked elements, situations, processes, and their dynamics. However, many studies have shown that occupancy information is a major leading source of uncertainty when developing control approaches. Comprehensive and real‐time fine‐grained occupancy information has to be, therefore, integrated in order to improve the performance of occupancy‐driven control approaches. The work presented in this paper is toward the development of a holistic platform that combines recent IoT and Big Data technologies for real‐time occupancy detection in smart building. The purpose of this work focuses mainly on the presence of occupants by comparing both static and dynamic machine learning techniques. An open‐access occupancy detection dataset was first used to assess the usefulness of the platform and the effectiveness of static machine learning strategies for data processing. This dataset is used for applications that follow the strategy aiming at storing data first and processing it later. However, many smart buildings' applications, such as HVAC and ventilation control, require online data streams processing. Therefore, a distributed real‐time machine learning framework was integrated into the platform and tested to show its effectiveness for this kind of applications. Experiments have been conducted for ventilation systems in energy‐efficient building laboratory (EEBLab) and preliminary results show the effectiveness of this platform in detecting on‐the‐fly presence of occupants, which is required to either make ON or OFF the system and then activate the corresponding embedded control technique (eg, ON/OFF, PID, state‐feedback).

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Occupancy Detection from Electricity Consumption Data

Cited by 196 publications

References 22 publications

Household occupancy monitoring using electricity meters

Household occupancy monitoring using electricity meters

Computational Topology to Monitor Human Occupancy

A platform architecture for occupancy detection using stream processing and machine learning approaches

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