Tatsiana G. Zoryna, Olga I. Yurkevich, Pavel A. Kabanov

Institute of power engineering of NAS of Belarus, Vitebskenergo RUE

The paper presents a comprehensive study of the problem of tariff policy optimization in the energy sector of the Republic of Belarus with a proposal to introduce innovative methods of predictive modeling of heat loads using neural networks. The authors conduct a detailed analysis of the current state of tariff policy in Belarus, identifying the key problem  a high level of cross-subsidization, with electricity tariffs for industry significantly exceeding those for households, which significantly affects the competitiveness of enterprises. The article notes that the main share of cross-subsidization in electricity tariffs for legal entities is the underpayment of consumers to the level of reasonable costs for heat energy. The study reveals the imperfection of the existing methods of heat load forecasting, which are based mainly on statistical data and do not take into account many dynamic factors. As a solution, we propose an innovative neural network forecasting technology that uses a hybrid architecture of deep neural networks combining the advantages of recurrent neural networks with long short-term memory and convolutional neural networks. The novelty of the study lies in the development of a comprehensive approach to the optimization of tariff policy through improving the accuracy of heat load forecasting, which allows to optimize the operation modes of heat generating equipment, minimize transportation losses and reduce operating costs. The authors note that the introduction of the proposed technology can reduce specific fuel consumption by 8-12% and reduce operating costs by 15-20%, creating a significant reserve for reducing tariffs without affecting the financial stability of energy companies. The results of the study have practical significance for reforming the tariff policy in the energy sector of the CIS countries and improving the competitiveness of national economies.

energy sector of Belarus, heat supply, heat power engineering, heat load forecasting, predictive modeling of heat loads, neural network forecasting of heat loads, tariff policy in the energy sector, energy tariffs