The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.

The operation of modern housing infrastructure is characterized by a constant increase in the cost of the limited resources used. This necessitates the priority implementation in the concept of a smart home of elements aimed at resource saving and their rational management. The study provides an overview of the implementation architectures of the internet of things (IoT) concept in the construction of home automation systems and the requirements they impose on the implementation of smart primary meters of controlled physical quantities. Based on a diversion analysis, a promising smart water meter was developed. The prototype is ergonomic and has a structural form factor convenient for further integration. The designed model of the electronic module of the water flow monitoring system implements, in addition to typical tasks, additional functionality: transfer of recorded indicators and technical information to the cloud storage, warning the user about an emergency situation, accumulation of current data in non-volatile memory. It is possible to use the accumulated statistics for training the predictive analysis module. The proposed architecture option will allow creating energy-efficient elements of home automation systems in the future.

This paper covers design and implementation of automated electromechanical drives for solar panels. It substantiates the need for using these devices when ensuring highly efficient generation of electricity from renewable energy sources (RES) is required and when human participation in the deployment of the device is impossible. The paper also considers the possibilities of increasing the efficiency of mobile solar power stations due to their automatic positioning during deployment and tracking the motion of the Sun in the sky through automatic control by the microcontroller-based electric drive directed by the incoming signals of the maximum irradiance tracking. The results of experimental tests of the developed microcontroller-based electromechanical drive are presented.

The article analyzes the development and formation of power supply in various countries. The development of the electric power industry in the countries is briefly analyzed and the events impacting it are presented. The main system indicators influencing the structure and implementation of their electric power complex are considered. The classification of the electricity consumer market is offered. Such indicators of electricity consumers as power, location, dynamic performance, reliability requirements, etc. are analyzed. The possibility of using renewable energy sources for the most complete satisfaction of consumers' requirements is considered. The classification of electricity consumers, their parameters, and recommendations for implementation of power supply with due account to the specifics of electrical energy consumption are given. The importance of individual power supply parameters for different consumers is shown in the radar chart. Conclusions are made about the need to assess electricity consumers according to the developed classification and highlight a unique set of parameters specific to a particular consumer to improve efficiency of implementation of its power supply.