Improving energy efficiency in building system using a novel people localization system

D’Aloia, Matteo; Cortone, Fabrizio; Cice, Gianpaolo; Russo, Ralph P.; Rízzí, María; Longo, Annalisa

doi:10.1109/eesms.2016.7504811

Cited by 12 publications

(10 citation statements)

References 12 publications

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“…In monitor-based systems, the location of a user or entity is passively obtained relative to some anchor node [1]. This can be utilized, for example, to enhance the energy efficiency of buildings by automatically switching-off lighting and heating/cooling in empty rooms [3,4]. In contrast, in device-based systems, the location information is obtained from a user-centric perspective [1], which can be utilized, for example, to enable navigation [5,6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection

Laska

Blankenbach

Klamma

2020

Sensors

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The accuracy of fingerprinting-based indoor localization correlates with the quality and up-to-dateness of collected training data. Perpetual crowdsourced data collection reduces manual labeling effort and provides a fresh data base. However, the decentralized collection comes with the cost of heterogeneous data that causes performance degradation. In settings with imperfect data, area localization can provide higher positioning guarantees than exact position estimation. Existing area localization solutions employ a static segmentation into areas that is independent of the available training data. This approach is not applicable for crowdsoucred data collection, which features an unbalanced spatial training data distribution that evolves over time. A segmentation is required that utilizes the existing training data distribution and adapts once new data is accumulated. We propose an algorithm for data-aware floor plan segmentation and a selection metric that balances expressiveness (information gain) and performance (correctly classified examples) of area classifiers. We utilize supervised machine learning, in particular, deep learning, to train the area classifiers. We demonstrate how to regularly provide an area localization model that adapts its prediction space to the accumulating training data. The resulting models are shown to provide higher reliability compared to models that pinpoint the exact position.

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Section: Introductionmentioning

confidence: 99%

Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection

Laska

Blankenbach

Klamma

2020

Sensors

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“…In medical signal processing, the accurate diagnosis and/or assessment of a disease depends on both signal acquisition and interpretation. Medical signal interpretation processes can benefit from computer technology [4][5][6]. It can help clinicians to screen abnormalities and the risks associated with them and contribute to the diagnosis of clinical signs for the purpose of recognizing the nature and cause of the pathological event [7,8].…”

Section: Introductionmentioning

confidence: 99%

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

2019

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The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

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“…The former does not obtain good results since simulations do not consider spontaneous and unplanned modifications in environmental conditions. However, systems that use real-time data, either from user tracking or contextual information, even price of energy, make these data available to users, allowing for a substantial improvement in the effectiveness of the designed solutions [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the most common solutions in this area are focused on the search for patterns in the use of lighting, windows, blinds or heating [ 3 , 15 ]. Some works go further and use the position of users to determine richer and more accurate behaviour patterns [ 16 , 18 ]. Moreover, the implantation of smart meters in homes is becoming universal [ 16 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring

García

Prieto

Alonso

et al. 2017

Sensors

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Real-time Localization Systems have been postulated as one of the most appropriated technologies for the development of applications that provide customized services. These systems provide us with the ability to locate and trace users and, among other features, they help identify behavioural patterns and habits. Moreover, the implementation of policies that will foster energy saving in homes is a complex task that involves the use of this type of systems. Although there are multiple proposals in this area, the implementation of frameworks that combine technologies and use Social Computing to influence user behaviour have not yet reached any significant savings in terms of energy. In this work, the CAFCLA framework (Context-Aware Framework for Collaborative Learning Applications) is used to develop a recommendation system for home users. The proposed system integrates a Real-Time Localization System and Wireless Sensor Networks, making it possible to develop applications that work under the umbrella of Social Computing. The implementation of an experimental use case aided efficient energy use, achieving savings of 17%. Moreover, the conducted case study pointed to the possibility of attaining good energy consumption habits in the long term. This can be done thanks to the system’s real time and historical localization, tracking and contextual data, based on which customized recommendations are generated.

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Improving energy efficiency in building system using a novel people localization system

Cited by 12 publications

References 12 publications

Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection

Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring

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