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.
This work analyses the factors affecting the capacity of rechargeable chemical electrical power sources. Primarily, it is focused on the classification of these factors and the development of recommendations for increasing the capacity of batteries. We propose an original approach to the Li-ion rechargeable batteries operation, that is mandatory input control and several charge/discharge cycles before placing Li-ion accumulator battery into the operating device. We propose a method for periodic control of Li-ion accumulator batteries in particularly important devices and for the concept of predictive repair. The charge volume loss during operation of a Li-ion rechargeable battery at sub-zero temperatures was assessed quantitatively. The engineered method and the created device can be used when operating a Li-ion accumulator battery at sub-zero temperatures, in the far north, or in the case of increased reliability requirements for devices running on Li-ion rechargeable batteries. Further research will be aimed at the automated monitoring of the state of Li-ion accumulator battery, as well as the possibility of integrating this development into devices using Li-ion accumulator battery in advanced smart systems.
This paper concentrates on the organizational and communication aspects of development of the smart grid technologies. The paper highlights the potential of decentralised electricity generation for generating electricity from less energy-intensive and cost-efficient sources. It shows that renewable and unconventional energy sources may be integrated into decentralised electricity grids – the generation lines that have an intelligent grid. In addition, the paper focuses on the benefits and risks of different smart grid applications and their impact. We show that smart grids have the potential to minimise costs, but the use of smart grid technology also affects the level of risk, so the organizational and communication aspects are of a great importance.
<span lang="EN-US">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.</span>
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.
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