Open AccessModeling of Seasonal Variations of Thermal Energy Production by an Electric Power Company based on Neural Network Technology
Author(s) -
Vasiliy V. Zhebsain,
O P Erdniev,
T V Zhebsain
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2096/1/012112
Subject(s) - electric power , artificial neural network , production (economics) , thermal energy , thermal , latitude , population , power (physics) , electric potential energy , electric energy , business , environmental economics , environmental science , meteorology , computer science , economics , geography , microeconomics , physics , thermodynamics , demography , geodesy , machine learning , sociology
The paper considers the problem of modeling the dependence of the value of thermal energy production by an electric power company on the air temperature using neural network technology. As an example of an electric power company producing thermal energy, the Public Joint-Stock Company (PJSC) Yakutskenergo. As consumers of thermal energy, organizations, enterprises and the population of the city of Yakutsk, are located at latitude 62 and characterized by a cold northern climate. The numerical experiments carried out in this paper have shown that the general trend of the temperature dependence of thermal energy production, observed empirically, is well described by a neural network