Novel Proposal for Prediction of CO2 Course and Occupancy Recognition in Intelligent Buildings within IoT

Abstract

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.

Indirect Recognition of Predefined Human Activities

“…KNX modules are commonly commissioned using the Engineering Tool Software (ETS). In addition to ETS, a .net-based software was developed [ 12 ] to ensure the connection of KNX-based devices and IBM cloud storage technology, which enables the communication between IBM Watson IoT platform and KNX smart installation. The measurements of CO 2 accumulation, indoor temperature, and humidity were performed using the MTN6005-0001 module.…”

“…Vanus et al [ 11 ] used the IBM SPSS modeler tool and neural networks for CO 2 prediction within smart home care. Vanus et al [ 12 ] compared neural networks, random trees, and linear regression for the purpose of indirect occupancy recognition in intelligent buildings. This paper proposes to employ an identical KNX-based setup building on the above contributions with a significant difference in expanding the occupancy monitoring to activity recognition.…”

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

“…The operation of the individual components of KNX technology is ensured by the means of group addresses ( Figure 3). The connection of KNX technology and IBM cloud technology is ensured in this work by our developed software [32], which enables the communication between IBM Watson IoT platform and KNX smart installation (Figure 4). Message queuing telemetry transport (MQTT) protocol is used as a communication protocol.…”

“…Therefore, a number of neurons do not significantly impact prediction accuracy. The complete and detailed analysis of these prediction results can be found in [32].…”

Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5.0 Decision Trees

“…The purpose of this study was to validate the performance of balanced random trees (RTs) and boosted C5.0 decision tree (DTs) models for classifying the biodegradable substances based on two-dimensional molecular descriptors. The RT model is a powerful predictive algorithm for classification and regression purposes with several successful applications [ 5 , 6 , 7 ]. Random trees and random forest methodologies have the same meaning in the literature.…”