Using the IBM SPSS SW Tool with Wavelet Transformation for CO<sub>2</sub> Prediction within IoT in Smart Home Care
Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/6/1407 |
| _version_ | 1798000016196894720 |
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| author | Jan Vanus Jan Kubicek Ojan M. Gorjani Jiri Koziorek |
| author_facet | Jan Vanus Jan Kubicek Ojan M. Gorjani Jiri Koziorek |
| author_sort | Jan Vanus |
| collection | DOAJ |
| description | Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. The paper examines the possibilities of increasing the accuracy of CO<sub>2</sub> predictions in Smart Home Care (SHC) using the IBM SPSS software tools in the IoT to determine the occupancy times of a monitored SHC room. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods. The Radial Basis Function (RBF) method was applied to predict CO<sub>2</sub> levels from the measured indoor and outdoor temperatures and relative humidity. The most accurately predicted results were obtained from data processed at a daily interval. To increase the accuracy of CO<sub>2</sub> predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The prediction accuracy achieved in the selected experiments was greater than 95%. |
| first_indexed | 2024-04-11T11:13:33Z |
| format | Article |
| id | doaj.art-00c6f2513db1450fba7f3a9611010c75 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:13:33Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-00c6f2513db1450fba7f3a9611010c752022-12-22T04:27:19ZengMDPI AGSensors1424-82202019-03-01196140710.3390/s19061407s19061407Using the IBM SPSS SW Tool with Wavelet Transformation for CO<sub>2</sub> Prediction within IoT in Smart Home CareJan Vanus0Jan Kubicek1Ojan M. Gorjani2Jiri Koziorek3Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava 70800, Czech RepublicDepartment of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava 70800, Czech RepublicDepartment of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava 70800, Czech RepublicDepartment of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava 70800, Czech RepublicStandard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. The paper examines the possibilities of increasing the accuracy of CO<sub>2</sub> predictions in Smart Home Care (SHC) using the IBM SPSS software tools in the IoT to determine the occupancy times of a monitored SHC room. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods. The Radial Basis Function (RBF) method was applied to predict CO<sub>2</sub> levels from the measured indoor and outdoor temperatures and relative humidity. The most accurately predicted results were obtained from data processed at a daily interval. To increase the accuracy of CO<sub>2</sub> predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The prediction accuracy achieved in the selected experiments was greater than 95%.https://www.mdpi.com/1424-8220/19/6/1407Smart Home Care (SHC)monitoringpredictiontrend detectionArtificial Neural Network (ANN)Radial Basis Function (RBF)Wavelet Transformation (WT)SPSS (Statistical Package for the Social Sciences) IBMIoT (Internet of Things)Activities of Daily Living (ADL) |
| spellingShingle | Jan Vanus Jan Kubicek Ojan M. Gorjani Jiri Koziorek Using the IBM SPSS SW Tool with Wavelet Transformation for CO<sub>2</sub> Prediction within IoT in Smart Home Care Sensors Smart Home Care (SHC) monitoring prediction trend detection Artificial Neural Network (ANN) Radial Basis Function (RBF) Wavelet Transformation (WT) SPSS (Statistical Package for the Social Sciences) IBM IoT (Internet of Things) Activities of Daily Living (ADL) |
| title | Using the IBM SPSS SW Tool with Wavelet Transformation for CO<sub>2</sub> Prediction within IoT in Smart Home Care |
| title_full | Using the IBM SPSS SW Tool with Wavelet Transformation for CO<sub>2</sub> Prediction within IoT in Smart Home Care |
| title_fullStr | Using the IBM SPSS SW Tool with Wavelet Transformation for CO<sub>2</sub> Prediction within IoT in Smart Home Care |
| title_full_unstemmed | Using the IBM SPSS SW Tool with Wavelet Transformation for CO<sub>2</sub> Prediction within IoT in Smart Home Care |
| title_short | Using the IBM SPSS SW Tool with Wavelet Transformation for CO<sub>2</sub> Prediction within IoT in Smart Home Care |
| title_sort | using the ibm spss sw tool with wavelet transformation for co sub 2 sub prediction within iot in smart home care |
| topic | Smart Home Care (SHC) monitoring prediction trend detection Artificial Neural Network (ANN) Radial Basis Function (RBF) Wavelet Transformation (WT) SPSS (Statistical Package for the Social Sciences) IBM IoT (Internet of Things) Activities of Daily Living (ADL) |
| url | https://www.mdpi.com/1424-8220/19/6/1407 |
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