No abstract

Aims of this research are development of a complex statistical analysis algorithm for active electric power consumption data, consumption of energy resources and manufacturing products, implementation of statistical analysis in practice. Proposed parameters and criteria, which can help to technical staff in factories, to provide optimal and economical operating of supply and distribution systems as electricity, water, gas, heat, compressed air, etc. for production facilities, based on the collected active electric power consumption data for previous periods, information about consumption dynamic. It is concluded that the statistical analysis of the data, obtained for each type of engineering equipments (water supply and sewage, supply systems of compressed air, gas, electricity and steam) and various consumables coefficients (in the proposed algorithm) make possible to identify "weak areas" and to determine the most rational ways to optimize energy usage.

The problem of early and accurate forecasting of electricity consumption is acute for the unified energy system of Ukraine. With successful forecasting of consumption, which is based on many aspects, it is possible to buy electricity/losses in different market segments much more profitably, saving large amounts of money, which can then be directed to the development and modernization of electricity networks. This has always been an urgent issue, but today, when a large part of Ukraine's energy equipment has been destroyed by Russian missiles, it has become even more painful. The use of the method of artificial neural networks (ANN) for short-term forecasting of electricity consumption is considered. It was established that ANN can be used to make a forecast of electricity consumption a day ahead with an error of 4.86% compared to the actual amount of electricity consumption. Performing a comparison of forecast values with actual values allows us to talk about the adequacy of the selected forecasting model and its application in practice for the successful operation of energy supply companies in the electricity market.

The use of centralized and decentralized electricity supply schemes, the introduction of feedback between the consumer of electricity and its producer led to a rapid increase in information flows at all stages of the functioning of the electricity industry of Ukraine, starting with the production of electricity and before its consumption. This necessitates the processing, storage and transmission of large data sets. On the other hand, the time constraints imposed on information processing and decision-making efficiency, as well as the limited bandwidth of communication networks require effective optimization of information flows in terms of their compression and compact storage, transmission and recovery without loss. At the same time, the determining quantitative and qualitative information parameters are electricity consumption modes and the quality of electric energy, and the main mode parameter for solving many problems of planning, management and carrying out commercial calculations is the graphs of electric load. Based on the analysis of the functional relationships of wavelet coefficients according to the levels of wavelet decomposition, the robot presents a modified reconstruction scheme and developed a model based on which a method of local (segmental) restoration of information signals according to the levels of wavelet detail is synthesized using the example of the electric load graph. In the paper, a model of local recovery of time segments of information signals is proposed and a sequential algorithm of actions synthesized on its basis. This technique allows for effective segmentation of information signals, reduces the duration of mathematical processing, simplifies database analysis, increases the effectiveness of controlling the reliability of initial data recovery by reserving recovery paths with subsequent comparison of results.

The purpose of this work is to solve the problem of optimizing the management of maintenance and repair equipment at large enterprises. In a unified management system, the operational collection, consolidation and transfer of indicators about the state of all numerous power equipment allows solving and sometimes avoiding many problems. It is about reducing the time for repair work (equipment downtime), offloading personnel, optimizing logistics chains, and reducing material costs. The general problems of creating intelligent energy systems from the point of view of information and telecommunication technologies have been studied. A way to process information flows in the monitoring and management of intelligent energy systems modes is proposed, which involves combining information and mathematical technologies and the use of international data standards. The approach to the development of a new information and technological infrastructure of intelligent energy systems is considered. After conducting the analysis, the optimal system is seen as a multi-level control system for intelligent electric power systems. The technology combines intelligent tools for situation analysis and software systems for mode modeling and control. The use of IT infrastructure allows to create a single information space that includes data and knowledge, as well as a set of mathematical models and methods for solving the problems of the electric power industry in the conditions of active adaptive management. In the conclusions, a detailed classification of the types of information about mode parameters is provided and the relationship between the quality of mode information and the application of various mathematical models is determined.