Novel proposal for prediction of CO2 course and occupancy recognition in Intelligent Buildings within IoT

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%.Web of Science1223art. no. 454

Lighting control network based on KNX protocol, for the reduction of energy consumption

This article presents the development of a lighting control network to reduce the energy consumption of a commercial building, using the KNX protocol; because of the high rates of electricity consumption, the same that are reflected in the payment of the electricity supply. For this, the design of the network architecture is carried out, the tree type quality and it has KNX, DALI components and LED luminaires, which are interconnected by means of an Ethernet type BUS; The KNX protocol configuration is then performed using the ETS version 5 software; carries out the implementation of KNX technology, determines the reduction of energy consumption by 82.33%. Likewise, emissions of carbon dioxide (CO 2), one of the main gases involved in climate change, were reduced by 85%. With these results we obtain economic and environmental benefits; Reason why it is proposed to perform the same procedure for the control of air conditioning systems, since their operation represents 32.8% of the energy consumption of an establishment

The design of an indirect method for the human presence monitoring in the intelligent building

This article describes the design and verification of the indirect method of predicting the course of CO2 concentration (ppm) from the measured temperature variables Tindoor (degrees C) and the relative humidity rH(indoor) (%) and the temperature T-outdoor (degrees C) using the Artificial Neural Network (ANN) with the Bayesian Regulation Method (BRM) for monitoring the presence of people in the individual premises in the Intelligent Administrative Building (IAB) using the PI System SW Tool (PI-Plant Information enterprise information system). The CA (Correlation Analysis), the MSE (Root Mean Squared Error) and the DTW (Dynamic Time Warping) criteria were used to verify and classify the results obtained. Within the proposed method, the LMS adaptive filter algorithm was used to remove the noise of the resulting predicted course. In order to verify the method, two long-term experiments were performed, specifically from February 1 to February 28, 2015, from June 1 to June 28, 2015 and from February 8 to February 14, 2015. For the best results of the trained ANN BRM within the prediction of CO2, the correlation coefficient R for the proposed method was up to 92%. The verification of the proposed method confirmed the possibility to use the presence of people of the monitored IAB premises for monitoring. The designed indirect method of CO2 prediction has potential for reducing the investment and operating costs of the IAB in relation to the reduction of the number of implemented sensors in the IAB within the process of management of operational and technical functions in the IAB. The article also describes the design and implementation of the FEIVISUAL visualization application for mobile devices, which monitors the technological processes in the IAB. This application is optimized for Android devices and is platform independent. The application requires implementation of an application server that communicates with the data server and the application developed. The data of the application developed is obtained from the data storage of the PI System via a PI Web REST API (Application Programming Integration) client.Web of Science8art. no. 2

Occupancy detection in smart home space using interoperable building automation technologies

To detect whether people are occupying individual rooms in a smart home, a range of sensors and building automation technologies can be employed. For these technologies to function in tandem and exchange useful data in a smart home environment, they must be interoperable. The article presents a new interoperable solution which combines existing decentralized KNX building automation technology with a KNX/LabVIEW software application gateway using visible light communication to track occupancy in a room. The article also describes a novel KNX/IoT software application gateway which uses an MQTT protocol for interoperability between KNX technology and IBM Watson IoT platform. We conducted an experiment with the originally designed solution to detect occupancy in an office room. We used KNX and BACnet building automation technology to produce an interoperable KNX/BACnet hardware gateway which allowed the application of artificial neural network mathematical methods for CO2 waveform prediction. The best results in detecting occupancy in a room were R = 0.9548 (Levenberg-Marquardt algorithm), R = 0.9872 (Bayesian regularization algorithm), and R = 0.8409 (scaled conjugate gradient algorithm), which correspond to the results obtained by other authors and a minimum system prediction accuracy of 96%.Web of Science12art. no. 4

Monitorování lidského chování v Smart Home (SH) v rámci IoT

It is widely researched that last 20 years fall accidents increased dramatically. Most often it happens with elderly people who leave alone. Without timely assistance, fall accident may lead to unpredictable consequences. Nowadays smart home technologies allow not only monitor but collect and process human behavior data. This diploma thesis applies cutting edge technologies to collect data from smart home based on KNX technology with help of Home Assistant software, preprocess it with a machine learning model stored in Amazon Web Service and notify emergency about fall event via Telegram messenger.Je široce prozkoumáváno, že za posledních 20 let se nehody s poklesem dramaticky zvýšily. Nejčastěji se to stává u starších lidí, kteří odcházejí sami. Bez včasné pomoci může nehoda s pádem vést k nepředvídatelným následkům. Technologie inteligentní domácnosti dnes umožňují nejen sledovat, ale také shromažďovat a zpracovávat údaje o lidském chování. Tato diplomová práce využívá nejmodernější technologie ke sběru dat z inteligentní domácnosti založené na technologii KNX pomocí softwaru Home Assistant, jejich předzpracování pomocí modelu strojového učení uloženého ve službě Amazon Web Service a nouzové upozornění na událost pádu prostřednictvím telegramového posla.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř

Application of a new CO2 prediction method within family house occupancy monitoring

The article describes the application of Python for verification of a newly designed method of CO2 prediction from measurements of indoor parameters of temperature and relative humidity within occupancy monitoring in real conditions of a family home. The article describes the implementation of non-electric quantities (indoor CO2 concentration, indoor temperature, indoor relative humidity) measurement in five rooms of a family home (living room, kitchen, children's room, bathroom, bedroom) using Loxone technology sensors. The IBM IoT (Internet Of Things) was used for storing and subsequent processing of the measured values within the time interval of December 22, 2018, to December 31, 2018. The devised method used radial basis function (artificial neural networks (ANN)) mathematical method (implementation in Python environment) to perform accurate predictions. For further increase of the accuracy and reduction of prediction noise from the obtained course of the predicted signal, multiple variations of the LMS adaptive filter algorithm (Sign, Sign-Sign, Sign-Regressor) were used (implemented in the MATLAB SW tool). The accuracy of the newly proposed CO2 concentration prediction method exceeds 95% in the selected experiments.Web of Science915877215876

Enhancing the efficiency of electricity utilization through home energy management systems within the smart grid framework

The concept behind smart grids is the aggregation of “intelligence” into the grid, whether through communication systems technologies that allow broadcast/data reception in real-time, or through monitoring and systems control in an autonomous way. With respect to the technological advancements, in recent years there has been a significant increment in devices and new strategies for the implementation of smart buildings/homes, due to the growing awareness of society in relation to environmental concerns and higher energy costs, so that energy efficiency improvements can provide real gains within modern society. In this perspective, the end-users are seen as active players with the ability to manage their energy resources, for example, microproduction units, domestic loads, electric vehicles and their participation in demand response events. This thesis is focused on identifying application areas where such technologies could bring benefits for their applicability, such as the case of wireless networks, considering the positive and negative points of each protocol available in the market. Moreover, this thesis provides an evaluation of dynamic prices of electricity and peak power, using as an example a system with electric vehicles and energy storage, supported by mixed-integer linear programming, within residential energy management. This thesis will also develop a power measuring prototype designed to process and determine the main electrical measurements and quantify the electrical load connected to a low voltage alternating current system. Finally, two cases studies are proposed regarding the application of model predictive control and thermal regulation for domestic applications with cooling requirements, allowing to minimize energy consumption, considering the restrictions of demand, load and acclimatization in the system

QuiiQ automation foundation

Tese de mestrado. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 200

IPv6 Addressing Proxy: Mapping Native Addressing from Legacy Technologies and Devices to the Internet of Things (IPv6)

Sensors utilize a large number of heterogeneous technologies for a varied set of application environments. The sheer number of devices involved requires that this Internet be the Future Internet, with a core network based on IPv6 and a higher scalability in order to be able to address all the devices, sensors and things located around us. This capability to connect through IPv6 devices, sensors and things is what is defining the so-called Internet of Things (IoT). IPv6 provides addressing space to reach this ubiquitous set of sensors, but legacy technologies, such as X10, European Installation Bus (EIB), Controller Area Network (CAN) and radio frequency ID (RFID) from the industrial, home automation and logistic application areas, do not support the IPv6 protocol. For that reason, a technique must be devised to map the sensor and identification technologies to IPv6, thus allowing homogeneous access via IPv6 features in the context of the IoT. This paper proposes a mapping between the native addressing of each technology and an IPv6 address following a set of rules that are discussed and proposed in this work. Specifically, the paper presents a technology-dependent IPv6 addressing proxy, which maps each device to the different subnetworks built under the IPv6 prefix addresses provided by the internet service provider for each home, building or user. The IPv6 addressing proxy offers a common addressing environment based on IPv6 for all the devices, regardless of the device technology. Thereby, this offers a scalable and homogeneous solution to interact with devices that do not support IPv6 addressing. The IPv6 addressing proxy has been implemented in a multi-protocol Sensors 2013, 13 6688 card and evaluated successfully its performance, scalability and interoperability through a protocol built over IPv6