Development of an Intelligent System for the Prognostication of Energy Produced by Photovoltaic Cells in Smart Grid Systems

Solution of the problem of prognostication of the generated energy was proposed on the basis of mathematical apparatus of neural-fuzzy networks. The conceptual model of the household information system as a part of the common SMART GRID system was proposed. The main task of this system is continuous monitoring of the power net, prognostication of consumption of the energy consumed by domestic appliances and the energy produced by photovoltaic cell panels. Current and predicted data were obtained based on the use of current sensors and mathematical apparatus of neural-fuzzy logic. Importance and necessity of using SMART GRID technology for improving efficiency of power net operation was shown. Application of such systems can reduce energy costs and the environmental impact of energy systems. This effect is achieved by prognostication of the energy generated by domestic renewable energy sources, in particular photovoltaic cell panels which ensures more efficient energy management.Also, the proposed model of the information system makes it possible to account produced and consumed energy which enables creation of an energy-efficient operation schedule of household appliances. Analysis of the dependence of the forecast accuracy on the choice of input characteristics was made. As a result, the optimal number of neurons in the inner layer was empirically set to 250 with a prediction error within 5 %. Influence of weather factors on accuracy of the resulting forecasts was considered. In particular, it has been found that quite significant differences between actual and projected data (up to 12 %) are due to the inaccuracy of local forecasts. The proposed information model can be integrated into existing or designed systems of the Smart Home type

Defining and modeling of students’ professional thinking development dependence on their training process organization

The professional thinking issues are analyzed in the research. The authors pointed that the technical thought concepts, images and practical actions are in a complex and dynamic interaction with each other. The components of professional thinking are considered in detail. The training method based on the implementation of forming influence is proposed. The regression analysis of the students’ academic progress indicators who are trained by the traditional and innovative methodology with forming influence is conducted in the article. Analysis of thinking activity development levels in the process of professional tasks solving performed by the students of the control and experimental groups demonstrated the straight-line correlation dependence of the professional thinking development on the organization of professional activity in general and the training organization in particular

The Concept of a Modular Cyberphysical System for the Early Diagnosis of Energy Equipment

We have proposed a concept of the modular cyberphysical system for the early diagnosis of industrial and household power equipment based on the application of approaches and standards of Industry 4.0, in particular the concept of the Internet of Things. The main task of the concept and approaches proposed in this paper is the indirect diagnosis and identification of any power equipment whose basic element is the asynchronous motor, in particular the identification of failures and excessive power consumption. In order to resolve the set tasks, it is proposed to use a modular structure of Smart Box diagnosed devices. Specifically, we demonstrate a model of the modular cyberphysical system using a Smart Box device for the early diagnosis of electric equipment, as well as its information flows. This makes it possible to divide all the technological objects at an enterprise into separate structural units, which could form a part of the information cluster. That reduces the reaction time in a cluster system by 30‒35 % compared to a standard one. In addition, the use of a given type of the system makes it possible to reduce the quantity of specialized equipment to the application of similar power equipment.It is proposed to use as a computational core of a Smart Box device the structure a neuro-fuzzy network, which consists of 5 layers. A special feature of this system is the capability to change the number of terms for input variables in order to improve the quality of identification of induction motors. We have chosen, as informative attributes, the characteristic frequencies, which identify an electric motor in the power grid. Specifically, for the systems with small generating capacity, in order to increase the diagnosed induction motors within a cluster, it is advisable to reduce the input set, for example, to 3‒4 CF.The results of our study, in the form of a model of the modular cyberphysical system could be used to build hardware and software modules for the diagnosis of technological and household electrical equipment. In turn, these modules could be combined into an overall global network of IoT