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An Approach to Forecasting the Structure of Energy Generation in the Age of Energy Transition Based on the Automated Determination of Factor Significance

Author

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  • Igor V. Ilin

    (Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia)

  • Oksana Yu. Iliashenko

    (Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia)

  • Egor M. Schenikov

    (Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia)

Abstract

In the age of energy transition that we are going through today, many research studies discuss how to develop various approaches to making forecasts aimed at obtaining quantitative assessments of the technical and economic indicators of the energy industry. This paper considers the adaptation of a comprehensive approach to forecasting the structure of energy generation based on the factor and trend approach and using autoregressive and multifactor models that apply a linear regression tool with ridge regularization. To implement this approach, we propose a tool for automated selection of the factors that have the most significant impact on the change in the structure of energy generation. This approach allows us to forecast the dynamics of electricity generation by different types of generating facilities as affected by the key factors in energy transition in the short, medium, and long term. As a result, we obtained quantitative relationships for the energy generation structure. Over the next 10 years, the share of generation using renewable energy sources will increase to 10%, and the share of thermal power plants, on the contrary, will decrease to 50%, despite the growth in demand for electricity. Also, greenhouse gas emissions will be reduced by 30%. We have also provided scientific justification for the sufficient reliability of the forecasts we present.

Suggested Citation

  • Igor V. Ilin & Oksana Yu. Iliashenko & Egor M. Schenikov, 2023. "An Approach to Forecasting the Structure of Energy Generation in the Age of Energy Transition Based on the Automated Determination of Factor Significance," Energies, MDPI, vol. 17(1), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:68-:d:1305194
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    References listed on IDEAS

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    1. Icaro Figueiredo Vilasboas & Julio Augusto Mendes da Silva & Osvaldo José Venturini, 2023. "On the Summarization of Meteorological Data for Solar Thermal Power Generation Forecast," Energies, MDPI, vol. 16(7), pages 1-10, April.
    2. Chen, Chaoyi & Pinar, Mehmet & Stengos, Thanasis, 2020. "Renewable energy consumption and economic growth nexus: Evidence from a threshold model," Energy Policy, Elsevier, vol. 139(C).
    3. Giuseppe Ciaburro & Gino Iannace, 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review," Data, MDPI, vol. 6(6), pages 1-30, May.
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