title: ÐÐ¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°ÑÐ¿ÐµÐºÑÐ° ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾ÑÑÐ¸ ÑÐµÐ³Ð¸Ð¾Ð½Ð°Ð»ÑÐ½ÑÑ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÐ¸ÑÑÐµÐ¼ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ¾ÑÐ¸Ð°Ð»ÑÐ½Ð¾-Ð´Ð¾ÑÑÑÐ¿Ð½Ð¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð² ÐºÐ¾Ð½ÑÐµÐºÑÑÐµ ÑÐºÐ¾Ð½Ð¾Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Monitoring the analytical aspect of the sustainability of regional electric power systems based on socially accessible information in the context of economic research
Creators: name: Ð¡Ð¼Ð¸ÑÐ½Ð¾Ð²Ð°, ÐÑÐ¸Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÐ½ÐµÑÐ³Ð¾Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ðµ
Ð´ÐµÑÐµÐ²ÑÑ ÑÐµÑÐµÐ½Ð¸Ð¹
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð²
ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ðµ Ð¸Ð½ÑÑÐ°ÑÑÑÑÐºÑÑÑÑ Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³Ð°
Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð² ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÐºÐµ
energy consumption
decision trees
time series forecasting
St. Petersburg infrastructure development
modeling in the energy sector

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description: The given work is devoted to developing tools for monitoring the stability of regional electricity systems using publicly available data. The research set the following goals: 1. Considering methods and tools for monitoring the stability of regional electric power systems. 2. Determining the advantages and disadvantages of the considered methods. 3. Identifying the most relevant vector of monitoring the characteristics of the stability of the energy system in the context of economic research. 4. Developing a tool for monitoring the sustainability of regional electric power systems using publicly available information. The relevance of the subject refers to the absence of high-quality analytical tools for processing and analyzing information regarding the state of the energy system, which serves as a central or critical parameter in economic and social studies conducted by an external analyst. The sources of information included data from domestic and international scientific literature, official internet resources, and analytical agencies. The use of a decision tree approach for processing and forecasting the trend in energy consumption is proposed by having based on the systematization of information regarding mathematical modeling in relation to the representation of data on energy consumption in the form of a time series. To improve the quality and accuracy of medium-term forecasts, a technique is being developed that involves incrementally expanding the training sample into blocks of predicted values. The proposed mathematical tools are implemented using the Python programming language.