Using the IBM SPSS SW Tool with Wavelet Transformation for CO2 Prediction within IoT in Smart Home Care

Vaňuš, Jan; Kubíček, Jan; Gorjani, Ojan Majidzadeh; Koziorek, Jiří

doi:10.3390/s19061407

Cited by 21 publications

(22 citation statements)

References 48 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 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 4 January 2020 doi:10.20944/preprints202001.0033.v1…”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%

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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

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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%

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

View full text Add to dashboard Cite

show abstract

“…For greater accuracy, it is necessary to implement filter algorithms [28] to remove additive noise from the predicted CO 2 concentration waveform. Based on the results achieved and described above, further experiments on CO 2 prediction will be conducted using more precise mathematical methods [29][30][31][32][33][34][37][38][39] within the IoT platform [40].…”

Section: Discussion-experiments 33a and 33bmentioning

confidence: 99%

“…It also gives more weight to larger differences. It is called the mean squared error as you are finding the average of a set of errors [29]:…”

Section: The Design Of the New Methods For Co 2 Predictionmentioning

confidence: 99%

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

Vaňuš

Nedoma

Fajkus

et al. 2020

Sensors

Self Cite

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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 an LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.

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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]. The disadvantage of using an LMS adaptive filter in an additive noise suppression application is the slow startup of the filtering process in the initial phase, depending on the step size parameter set and the adaptive filter order.…”

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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Using the IBM SPSS SW Tool with Wavelet Transformation for CO2 Prediction within IoT in Smart Home Care

Cited by 21 publications

References 48 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

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

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

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