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The effectiveness of the process of innovative transformation of human capital for the energy industry requires the development of science-based forecasting models. Special software has been developed for forecasting the development to manage changes at various stages of digital decision support. The components of the transformation model are structured on the basis of artificial neural network ensembles. To select a model for mapping the states of digital entities, graph theory is used. The control module of the state transition graph of digital entities is realized on the basis of processing of large weakly structured data. The differentiation of structural components by hierarchy levels with corresponding unifying properties of innovative development of digital solutions directly at the energy enterprises is carried out.

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Neural Network Models for Predicting Human Capital Development for the Energy Industry

  • 会议论文

  • 发表日期：

    2025年

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  • Aleksandr Zhukov,
  • Sergey Pronichkin,
  • Marina Krynzhina,
  • Evgeniy Maslenkin,
  • Ekaterina Melikhova,
  • Igor Kartsan

摘要

The effectiveness of the process of innovative transformation of human capital for the energy industry requires the development of science-based forecasting models. Special software has been developed for forecasting the development to manage changes at various stages of digital decision support. The components of the transformation model are structured on the basis of artificial neural network ensembles. To select a model for mapping the states of digital entities, graph theory is used. The control module of the state transition graph of digital entities is realized on the basis of processing of large weakly structured data. The differentiation of structural components by hierarchy levels with corresponding unifying properties of innovative development of digital solutions directly at the energy enterprises is carried out.