Development and implementation of novel sensor fusion algorithm for occupancy detection and automation in energy efficient buildings

Roselyn, J. Preetha; Uthra, R. Annie; Raj, Ajith; Devaraj, D.; Bharadwaj, Pranav; Kaki, Sai Vijay Deeraj Krishna

doi:10.1016/j.scs.2018.09.031

Cited by 43 publications

(21 citation statements)

References 17 publications

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“…Qualitative approaches include (1) classification of building components [19], (2) thermal bridge identification [20,21], (3) air leakage inspection [22], and (4) HVAC and pipeline system inspection [23]. Quantitative approaches include (1) U-value assessment [24,25], (2) moisture content identifi-cation [26], (3) thermal anomaly percentage calculation [16], and (4) indoor occupancy calculation for energy consumption inspection [27]. Recently, researchers have also installed thermal cameras onto other devices, such as unmanned aerial vehicles (UAVs) [28] and unmanned ground vehicles (UGVs) [29], to capture thermal images.…”

Section: Energy Aduitsmentioning

confidence: 99%

An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets

et al. 2021

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As-is building modeling plays an important role in energy audits and retrofits. However, in order to understand the source(s) of energy loss, researchers must know the semantic information of the buildings and outdoor scenes. Thermal information can potentially be used to distinguish objects that have similar surface colors but are composed of different materials. To utilize both the red–green–blue (RGB) color model and thermal information for the semantic segmentation of buildings and outdoor scenes, we deployed and adapted various pioneering deep convolutional neural network (DCNN) tools that combine RGB information with thermal information to improve the semantic and instance segmentation processes. When both types of information are available, the resulting DCNN models allow us to achieve better segmentation performance. By deploying three case studies, we experimented with our proposed DCNN framework, deploying datasets of building components and outdoor scenes, and testing the models to determine whether the segmentation performance had improved or not. In our observation, the fusion of RGB and thermal information can help the segmentation task in specific cases, but it might also make the neural networks hard to train or deteriorate their prediction performance in some cases. Additionally, different algorithms perform differently in semantic and instance segmentation.

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Section: Energy Aduitsmentioning

confidence: 99%

An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets

et al. 2021

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“…The research studies on occupancy detection approach are widely classified into sensor-based [33,34] and image-based [35,36] methods. The camera-based approaches are accurate but unable to detect occupancy when the target is in a static position [33]. The detection of occupants based on temperature and CO 2 concentration using sensors at the air return and supply vents is proposed in [34].…”

Section: Practical Applicationmentioning

confidence: 99%

Sensor fusion with high‐order moments constraints using projection‐based neural network

Alipouri

Bonab

Zhong

2021

IET Signal Processing

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The existing sensor fusion methods mainly follow two approaches, including Gaussian and Non-Gaussian-based sensor fusion approaches. In the first approach, fusion weights are determined based on the second moment. This approach is unable to account for high-order moments; thus, it is not accurate for non-Gaussian sensors. In the second approach, the fusion weights are determined using distribution functions of sensor data. Though this method is more accurate than Gaussian-based sensor fusion, it is a sophisticated method as it requires all moments information of each sensor, which is either not available or at least hard to be identified. Here, we propose an alternative way to determine the fusion weights by a limited number of n (>2) moment information of data. The proposed method makes trades off between accuracy and complexity. The other problem, which has not been studied in the literature, is existence of constraints on moments. The proposed method can address this problem as well. To do this, a projection-based neural network-based optimization method is used to calculate the optimal fusion weights that satisfy moment constraints. A practical application of the proposed sensor fusion method on predicting occupancy for heating, ventilation, and air conditioning (HVAC) is conducted.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…i. Presence (Imaging sensor) Roselyn et al [28]in their study used camera and thermal sensor to detect the occupancy presence accurately and used the information to control the lighting and fan on/off automatically. An image processing and a thermal sensor algorithm was proposed.…”

Section: Heating Ventilating and Air-conditioning (Hvac) And Occupancymentioning

confidence: 99%

A Taxonomy and Survey of Occupancy Detection Methods for Efficient Space and Energy Utilisation of Buildings

Kadir¹

2020

IJATCSE

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Human factor has a significant influence in space and energy utilisation in buildings. Space utilisation refers to a measure of whether space of a building is being used and how it is being used. While energy utilisation refers to the amount of energy being used. As space and energy of buildings are mostly dominated by the occupancy and related activities, occupancy information is vital in managing space and energy use. By detecting the presence of an occupant in a building or occupancy detection, space and energy can be utilised efficiently by corresponding to the occupant's dimension and activities, thus avoiding wastage. Accordingly, through various methods, occupancy detection employs various sensors available in term of spatial temporal properties depending on the area to be deployed. Majority of past studies were focused on the aspect of energy utilisation and occupancy and there is lack of review paper which gathers information on occupancy detection inrelation to both space and energy utilisation.Therefore, this paper aims to develop a taxonomy of occupancy detection technologies for efficient space and energy utilisation of buildings. A survey of past studies on space and energy utilisation of buildings in relation to occupancy level are done to understand the relationship between occupancy and space and energy utilisation, respectively. A taxonomy is then built to identify the technologies which are currently available for occupancy detection.

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Development and implementation of novel sensor fusion algorithm for occupancy detection and automation in energy efficient buildings

Cited by 43 publications

References 17 publications

An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets

An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets

Sensor fusion with high‐order moments constraints using projection‐based neural network

A Taxonomy and Survey of Occupancy Detection Methods for Efficient Space and Energy Utilisation of Buildings

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