New method for accurate prediction of CO2 in the Smart Home

Vaňuš, Jan; Martínek, Radek; Bilík, Petr; Žídek, Jan; Dohnálek, Pavel; Gajdoš, Petr

doi:10.1109/i2mtc.2016.7520562

Cited by 20 publications

(25 citation statements)

References 11 publications

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“…From the perspective of cost reduction, a method for predicting the CO2 concentration waveform by means of ANN SCG from the indoor temperature and indoor relative humidity values measured were devised and verified for the space location of an occupant (in rooms R104, R203, and R204) in the SH with the highest possible accuracy. The ANN SCG mathematical method used does not achieve such accuracy compared to the methods used in [28][29][30] and [34][35][36][37]. For selected experiments, nevertheless, the correlation coefficient in this article was greater than 90%.…”

Section: Discussionmentioning

confidence: 85%

“…For greater accuracy, it is necessary to implement filter algorithms [28] to remove additive noise from the predicted CO2 concentration waveform. Based on the results achieved and described above, further experiments on CO2 prediction will be conducted using more precise mathematical methods [29][30][31][32][33][34][35][36][37] within the IoT (Internet of Things) platform [38]. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 4 January 2020 doi:10.20944/preprints202001.0033.v1…”

Section: Discussion-experiments 33a and 33bmentioning

confidence: 99%

“…The final step is to compare the results and the plots, so as to decide which conditions and which methods are optimal for future predictions [34].…”

Section: Scaled Conjugate Gradient Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Vaňuš¹,

Nedoma²,

Fajkus³

et al. 2020

Preprint

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This article introduces a new way of using a Fibre Bragg Grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a Smart Home (SH). CO2 sensors are used to determine the CO2 concentration of the monitored rooms in an SH. CO2 sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO2 prediction, by means of an Artificial Neural Network (ANN) with a Scaled Conjugate Gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO2 concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO2 prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO2 prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the Adaptive Filtration with of LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.

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Section: Discussionmentioning

confidence: 85%

Section: Discussion-experiments 33a and 33bmentioning

confidence: 99%

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Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Vaňuš¹,

Nedoma²,

Fajkus³

et al. 2020

Preprint

Self Cite

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“…According to Zhao, occupancy of anomalous user behavior tends to be figured out from multiple time-series records of occupancy [43]. For prediction and subsequent classification of automatic human activity recognition (AR), the regression method of Artifical Neural Networks (ANN), Hidden Markov models [43][44][45] decision trees method [46], methods using Bayesian networks [47], Conditional Random Fields (CRF) or a sequential Markov Logic Network (MLN) [48] can be used.…”

Section: Second Part-the Optimized Artificial Neural Network Model Wimentioning

confidence: 99%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO 2 , humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO 2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg-Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO 2 concentrations from obtained measurements of temperature (indoor, outdoor) T i , T o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO 2 , T and rH with the aim to indirectly predict CO 2 leading to the elimination of CO 2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015ANN (1.6. -30.6.2015. For learned ANN (1.2.2014ANN (1.2. -27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO 2 for ADL monitoring in SHC.

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“…Measured values of nonelectrical and electrical quantities in real time using implemented KNX technology in SH (presence of persons, power consumption, temperature, relative humidity, or CO 2 concentration) need to be preprocessed and adjusted for subsequent calculations using appropriate mathematical methods (classification [10,11], recognition [12][13][14], and prediction [15]) (the prediction was performed by the ANN-based on the scaled conjugate gradient (SCG), experimental results verified high method accuracy > 90%), [16] (the prediction was performed by the ANN Bayesian regulation method (BRM) with LMS AF additive noise canceling, best accuracy was better than 95%) [17] (the prediction was performed by decision tree regression method with the accuracy of 46.25 ppm). An important area of the described chain is the suppression of additive noise from the measured and calculated waveforms of monitored quantities [18,19].…”

Section: Related Workmentioning

confidence: 99%

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

Vaňuš

Fiedorova

Kubíček

et al. 2020

Sensors

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The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.

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New method for accurate prediction of CO2 in the Smart Home

Cited by 20 publications

References 11 publications

Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Monitoring of the daily living activities in smart home care

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

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