INTELLECTUALIZATION OF ENERGY FACILITY MANAGEMENT BASED ON THE SMART GRID CONCEPT

This paper addresses the challenge of making control processes more intelligent within contemporary electric power systems, examined through the lens of the Smart Grid paradigm. Escalating electricity demand, unpredictable load fluctuations, and growing penetration of renewable generation into distribution networks have collectively undermined the adequacy of conventional regulatory approaches. Consequently, deploying adaptive, self-tuning systems with embedded forecasting capabilities at power facilities emerges as a pressing scientific and engineering priority. The study examines each stage of the data lifecycle—acquisition, transmission, and processing—underpinned by digital infrastructure. Particular attention is given to how real-time measurements from field sensors, advanced metering devices, and SCADA platforms are translated into actionable control decisions. A mathematical representation of the power system is formulated, upon which algorithms for demand forecasting and optimal parameter determination are constructed using artificial neural networks. System performance is evaluated through an integral quadratic cost function, and the effects on stability and energy efficiency are verified by simulation. Findings confirm that intelligent control measurably reduces network losses, mitigates fault occurrences, and strengthens supply continuity, thereby advancing the digital transformation of the energy sector.