Real-time building occupancy sensing using neural-network based sensor network

Ekwevugbe, Tobore; Brown, Neil; Pakka, Vijayanarasimha Hindupur; Fan, Denis

doi:10.1109/dest.2013.6611339

Cited by 70 publications

(39 citation statements)

References 16 publications

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“…The main work of the estimator design is to identify the model f (·), which is a regression problem. With enough training samples, which can be off-line measured, many machine learning techniques like ANN [3], [33]- [35], SVM [3], [33], [34], ELM [36], and deep learning [43] can be used to solve this regression problem. In this work, we select the ELM due to its simplicity, computational efficiency, and flexibility.…”

Section: Indoor Occupancy Estimation From Co2 Datamentioning

confidence: 99%

Indoor occupancy estimation from carbon dioxide concentration

Jiang

Masood

Soh

et al. 2016

Energy and Buildings

162

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This paper presents an indoor occupancy estimator with which we can estimate the number of real-time indoor occupants based on the carbon dioxide (CO2) measurement. The estimator is actually a dynamic model of the occupancy level. To identify the dynamic model, we propose the Feature Scaled Extreme Learning Machine (FS-ELM) algorithm, which is a variation of the standard Extreme Learning Machine (ELM) but is shown to perform better for the occupancy estimation problem. The measured CO2 concentration suffers from serious spikes. We find that pre-smoothing the CO2 data can greatly improve the estimation accuracy. In real applications, however, we cannot obtain the real-time globally smoothed CO2 data. We provide a way to use the locally smoothed CO2 data instead, which is real-time available. We introduce a new criterion, i.e.x-tolerance accuracy, to assess the occupancy estimator. The proposed occupancy estimator was tested in an office room with 24 cubicles and 11 open seats. The accuracy is up to 94 percent with a tolerance of four occupants.

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Section: Indoor Occupancy Estimation From Co2 Datamentioning

confidence: 99%

Indoor occupancy estimation from carbon dioxide concentration

Jiang

Masood

Soh

et al. 2016

Energy and Buildings

162

View full text Add to dashboard Cite

show abstract

“…24 With candidate features identified, the next step involved implementing the fusion strategy for occupancy estimation, see Figure 3). 25,26 Information theory Feature ranking for each variable in the sensing domain finds association strength between each feature and the number of occupants. Information theory is a widely used non-linear correlation measure for feature relevance analysis in machine learning applications.…”

Section: Data Processingmentioning

confidence: 99%

Advanced occupancy sensing for energy efficiency in office buildings

Ekwevugbe

Brown

Pakka

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part I:

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Control systems for heating, ventilation and air conditioning in non-domestic buildings often operate to fixed schedules, assuming maximum occupancy during business hours. Since lower occupancies usually mean less demand for heating, ventilation and air conditioning, energy savings could be made. Air quality sensing, often combined with temperature sensing, has performed sufficiently in the past for this if maintained properly, although sensor and control failures may increase energy use by as much as 50%. As energy costs increase, coupled with increased complexity in building services and reduced commissioning time, all placing ever higher demands on sensing, building controls must meet increasingly stringent environmental requirements, whilst also improving reliability. Sensor fusion offers performance and resilience to meet these demands, while cost and privacy are key factors which are also met. This article describes a neural network approach to sensor fusion for occupancy estimation. Feature selection was carried out using symmetrical uncertainty analysis, while fusion of sensor features used a back-propagation neural network, with occupant count accuracy exceeding 74%.

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“…temperature or air quality such as CO 2 , or a combination of them) were deployed in building scenarios and data was collected and processed for understanding the relationship between occupant behaviour patterns and energy consumption. With the introduction or real-time data collection techniques, new studies demonstrated how 'live' sensing approaches could be used to build data-driven models for occupancy detection [9,10]. These models however did not consider occupant comfort levels, and could not correctly predict occupancy (e.g.…”

Section: Occupancy and Activity Models Based On ML And Sensor Datamentioning

confidence: 99%

Modelling and Detection of User Activity Patterns for Energy Saving in Buildings

Ortega

Han

Bowring

2016

Emerging Trends and Advanced Technologies for Computational Intelligence

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Recently, it has been noted that user behaviour can have a large impact on the final energy consumption in buildings. Through the combination of mathematical modelling and data from wireless ambient sensors, we can model human behaviour patterns and use the information to regulate building management systems (BMS) in order to achieve the best trade-off between user comfort and energy efficiency. Furthermore, streaming sensor data can be used to perform real-time classification. In this work, we have modelled user activity patterns using both offline and online learning approaches based on non-linear multi-class Support Vector Machines. We have conducted a comparison study with other machine learning approaches (i.e. Linear SVM, Hidden-Markov and K-nearest models). Experimental results show that our proposed approach outperforms the other methods for the scenarios evaluated in terms of accuracy and processing speed.

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Real-time building occupancy sensing using neural-network based sensor network

Cited by 70 publications

References 16 publications

Indoor occupancy estimation from carbon dioxide concentration

Indoor occupancy estimation from carbon dioxide concentration

Advanced occupancy sensing for energy efficiency in office buildings

Modelling and Detection of User Activity Patterns for Energy Saving in Buildings

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