Systems for determining the position of objects inside buildings have a wide range of applications, such as the surveillance of people’s movements in hospitals, and of goods or mobile robots in warehouse spaces or production halls. Hence, there is a need for the development of methods that could be applied for those purposes. This paper presents the results of research on an experimental system for localizing people being evacuated from a building. The proposed solution was designed as a part of the building evacuation management system. The method used for finding location belongs to the class of proximity-type methods and is based on Received Signal Strength Indicator (RSSI) information of Bluetooth Low-Energy (BLE) devices. The devices used to build the system (BLE receivers) and the evacuee’s wristband (BLE transmitters) are low-budget electronic modules. The paper presents preliminary research and the process of selecting data processing methods, as well as the results of tests of the experimental network created for the evacuation system. The results of measurements and statistical analyses of the properties of the RSSI parameter of the BLE signal transmission between the modules used in the designed system are presented. In addition, the results of RSSI measurements and the analyses of RSSI recorded under varying environmental conditions in the building are presented. The choice of the data processing method and its parameters was made with the use of the determined probabilities of the nearest locator node detection. Finally, the performance of the experimental installation of the evacuee tracking system was tested and the effectiveness of the proximity method was evaluated. The experimental tests aimed to analyze the detection range and the impact of shading. They also allowed for determining the mean error and for estimating the maximum position determination error. It should be emphasized that the proposed position estimation method has a very low computational load, allowing the implementation of an extensive real-time system on a typical personal computer. Although the proposed system should be classified as a coarse positioning system, its features such as low cost, simplicity, flexibility, the use of commonly available components and low requirements for computational load make it attractive. Such a system is directly transferable to other applications in, for example, Industry 4.0.

The novel approach to the Low Energy Bluetooth RSSI (Received Signal Strength Indicator) examination for a personal location during the evacuation process is presented in this paper. The presented system is based on stationary locating localization nodes installed inside the facility and portable wristbands worn by people. A method based on the propagation model and preliminary determination of its characteristics is used to calculate the wristband-locator distance. The accuracy of the distance estimations is increased by assuming Gaussian model of RSSI measurements and using the estimator of RSSI mean value. A modified multilateration approach is used to estimate the person’s position in the 2D Cartesian coordinate system. The paper also includes the outcomes of experiments conducted on the proposed approach as it was applied to the prototype evacuation supervision system. The paper presents a comparison of the position estimation error for the proposed method based on the mean RSSI value estimator with the results obtained when raw RSSI values were used. Analysis and discussion of wristband position estimation error are also included.

This article presents the analysis of the influence of household appliances on the quality of the energy consumed by the end-user. The results of the research, then, concern the final consumer (the lowest level of the power grid). The research was conducted on 120 combinations of electrical appliances connected into a grid. Each combination consisted of three devices working simultaneously in a micro-grid. The obtained and statistically analyzed data proved that there are several types of appliances that have a great influence on the power quality (PQ) parameters changes. The results of the conducted experiments indicate the devices which influenced significantly the total harmonic distortion of voltage (THDV), the voltage frequency (FREQ) and the voltage fluctuation (V). Specific features of particular devices were examined in terms of their significance for the power quality deviation. This showed the most important features which should be considered while working out the prediction model. The future of smart grids resides in data analysis, predictive models and real-time optimization. One of the key characteristics is the reducing energy consumption generated by renewable energy sources. This phenomenon, namely looking for problems connected with sustainable power quality and their appropriate solution, is described in this article. We performed an extended analysis of the smart home appliances influence of individual quantities on a real model. Furthermore, we explored devices with a high impact on chosen power quality indicators. In the end, we discuss their specific behavior and relevance to the above-described phenomenon to improve the predictive model utility.

The production of thermal energy is one of the sources of environmental pollution, especially when it uses traditional fossil fuels (in particular hard and brown coal). Burning conventional fuels contributes to air pollution because of emitting gases and producing waste after the process of burning in the form of ash. The work below was aimed at determining the indirect emission of PAHs in the form of fly ash, depending on the type of fuel burned. The conducted research showed which solid fuel combustion content leaves the lowest content of PAH in the fly ash. This work contains the analysis and assessment of the content of 16 PAHs (polycyclic aromatic hydrocarbons) in fly ash from the combustion of five selected solid fuels in low-power boilers. The following fuels were chosen for the research: hard coal with granulation above 60 mm, coal with the grain size of 25–80 mm, coal with the grain size of 8–25 mm, pellets with the grain size of 6 mm, and mixed dry wood. The results of the research showed that the most frequent and most concentrated compounds were naphthalene and acenaphthylene from the PAH group. These hydrocarbons have the smallest number of rings in a molecule. It was also found that the content of the LMW (Low Molecular Weight Polycyclic Aromatic Hydrocarbons) fractions in the fly ash from the analyzed fuels in ∑PAHs exceeds 57% in the case of 6 mm pellets and in the case of the ash from wood and hard coal when the grain size >60 mm. The opposite dependence can be observed in the case of the HMW (High Molecular Weight Polycyclic Aromatic Hydrocarbons) fraction.