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Information × Registration Number 0225U002570, (0123U101692) , R & D reports Title Strategic directions, methods, and means of digitalization and intellectualization of energy systems using modern information and communication technologies popup.stage_title Розроблення методів інтелектуального енергоменеджменту в умовах ефективної інтеграції інформаційно-комунікаційних технологій та штучного інтелекту в енергоінфраструктуру Head Beshlei Mykola I., Доктор технічних наук Registration Date 19-03-2025 Organization Lviv Polytechnic National University popup.description1 Improving the efficiency of energy system operation in the conditions of integration with modern information and communication technologies and the use of artificial intelligence by developing new methods and software and hardware means for data transmission, processing, monitoring, management, and accounting at all stages of energy production, transportation, trade, and consumption. popup.description2  A hardware-software complex for the collection, transmission, monitoring, and analytics of end-user energy consumption parameters has been proposed, enabling the implementation of next-generation smart meters. Intelligent data collection nodes with support for energy-efficient communication channels have been implemented, ensuring continuous monitoring of energy system parameters and precise real-time accounting. A method for intelligent consumer profiling has been developed, taking into account their classes and priorities, which improves the accuracy of load forecasting on the energy system and enhances resource allocation mechanisms. The use of advanced clustering in combination with streaming analytics allows for the automatic grouping of consumers based on similar energy consumption characteristics, adapting to dynamic changes in their electricity usage profiles. A method for short-term and long-term electricity demand forecasting has been developed using deep neural networks, which minimizes the risks of network overload and ensures efficient balancing between generation and consumption. An intelligent approach to smoothing energy peaks based on dynamic pricing and adaptive control algorithms has been proposed, contributing to the reduction of overall network load. A method for forecasting the operating modes of energy infrastructure and detecting abnormal behavior of technological equipment, including power units and transformers, has been developed based on advanced deep learning algorithms. The proposed approach provides an in-depth analysis of spatiotemporal dependencies, allowing for the early detection of potential failures, minimizing the risks of emergency situations, and enhancing the reliability of the energy system through the implementation of predictive maintenance. Product Description popup.authors Beshlei Pavlo I. Kaidan Mykola V. Kyryk Marian I. Kochan Orest V. Medvetskyi Mykhailo B. Seliuchenko Marian O. Shkoropad Yurii I. popup.nrat_date 2025-03-19 Close