The prediction accuracy achieved in the selected experiments was greater than 95%.Īn intelligent building is one that is responsive to the requirements of occupants, organizations, and society. To increase the accuracy of CO 2 predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The most accurately predicted results were obtained from data processed at a daily interval. The Radial Basis Function (RBF) method was applied to predict CO 2 levels from the measured indoor and outdoor temperatures and relative humidity. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods. The paper examines the possibilities of increasing the accuracy of CO 2 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. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms.

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