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Forecasting the Power Generation Mix in Italy Based on Grey Markov Models

Author

Listed:
  • Guglielmo D’Amico

    (Department of Economics, “G. d’Annunzio” University, 65127 Chieti, Pescara, Italy
    These authors contributed equally to this work.)

  • Alex Karagrigoriou

    (Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece
    These authors contributed equally to this work.)

  • Veronica Vigna

    (Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, 66100 Chieti, Pescara, Italy
    These authors contributed equally to this work.)

Abstract

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey Model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which could provide powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Results show an improvement in the performance of more than 50% in terms of Root Mean Squared Error (RMSE) when the Markov chain is integrated in the analysis. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability.

Suggested Citation

  • Guglielmo D’Amico & Alex Karagrigoriou & Veronica Vigna, 2024. "Forecasting the Power Generation Mix in Italy Based on Grey Markov Models," Energies, MDPI, vol. 17(9), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2184-:d:1387887
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    References listed on IDEAS

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    1. De Rosa, Luca & Castro, Rui, 2020. "Forecasting and assessment of the 2030 australian electricity mix paths towards energy transition," Energy, Elsevier, vol. 205(C).
    2. Vlad Stefan Barbu & Guglielmo D’Amico & Riccardo Blasis, 2017. "Novel advancements in the Markov chain stock model: analysis and inference," Annals of Finance, Springer, vol. 13(2), pages 125-152, May.
    3. D'Amico, Guglielmo & Di Biase, Giuseppe & Manca, Raimondo, 2012. "Income inequality dynamic measurement of Markov models: Application to some European countries," Economic Modelling, Elsevier, vol. 29(5), pages 1598-1602.
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