The role of innovative software in increasing the efficiency of energy systems - Xilola Z. Nazirova; Saodat G‘.G‘oyibova (PROBLEMS OF ENERGY AND SOURCES SAVING, 2026)
Relevance: increasing efficiency in modern energy systems requires not only technical, but also digital solutions. In particular, there is a sharp increase in the need to introduce accurate, sustainable, and intelligent tools for identifying, analyzing, and forecasting electricity losses. In this process, innovative software tools that perform tasks such as real-time data processing, automatic selection of forecasting models, and working with cluster and anomalous data are of great importance. This is especially ensured by increasing energy efficiency in low-voltage networks, such as 0.4 kV distribution networks, and implementing precise monitoring and forecasting mechanisms. In such conditions, information systems with intelligent functions, created in modular and adaptive architecture, not only increase the efficiency of maintenance but also ensure the objectivity and speed of the decision-making process. Aim: development of innovative software that serves to automate the processes of analysis and forecasting of electricity losses in energy networks, scientific assessment of the effectiveness of the system based on its modular architecture, functional capabilities, and integrated forecasting models. Methods: in the modeling process, a comprehensive approach was applied, based on the integration of clustering, specialized neural networks for forecasting, and classical statistical methods that take into account the structure of data. Results: An information system for forecasting electricity losses - ISFEL in 0.4 kV distribution networks has been developed. Polynomial regression, ARIMA, GMDH, MLP, and hybrid forecasting methods were integrated into the system. This system is equipped with adaptive mechanisms that take into account the complexity and variability of the data structure, ensuring high accuracy and adaptability to operating conditions through the appropriate selection of a forecast model, visual analysis of results, and reduction of the influence of the human factor.