Modeling of Seasonal Variations of Thermal Energy Production by an Electric Power Company based on Neural Network Technology
Abstract 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...
| Published in: | Journal of Physics: Conference Series |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
IOP Publishing
2021
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1088/1742-6596/2096/1/012112 https://iopscience.iop.org/article/10.1088/1742-6596/2096/1/012112 https://iopscience.iop.org/article/10.1088/1742-6596/2096/1/012112/pdf |
| Summary: | Abstract 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 |
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