Wavelet-Based Filtration Procedure for Denoising the Predicted CO(2) Waveforms in Smart Home within the Internet of Things

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 CO(2) 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 CO(2) concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO(2) concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.