IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i9p2184-d1387887.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/9/2184/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/9/2184/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. De Rosa, Luca & Castro, Rui, 2020. "Forecasting and assessment of the 2030 australian electricity mix paths towards energy transition," Energy, Elsevier, vol. 205(C).
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Andrea Amado & Koji Kotani & Makoto Kakinaka & Shunsuke Managi, 2023. "Carbon tax for cleaner-energy transition: A vignette experiment in Japan," Working Papers SDES-2023-6, Kochi University of Technology, School of Economics and Management, revised Oct 2023.
    3. Guglielmo D'Amico & Riccardo De Blasis & Philippe Regnault, 2020. "Confidence sets for dynamic poverty indexes," Papers 2006.06595, arXiv.org.
    4. Brumana, Giovanni & Franchini, Giuseppe & Ghirardi, Elisa & Perdichizzi, Antonio, 2022. "Techno-economic optimization of hybrid power generation systems: A renewables community case study," Energy, Elsevier, vol. 246(C).
    5. Nolan, Tahlia, 2024. "Is pivoting offshore the right policy for achieving decarbonisation in the state of Victoria, Australia's electricity sector?," Energy Policy, Elsevier, vol. 190(C).
    6. Annala, Salla & Ruggiero, Salvatore & Kangas, Hanna-Liisa & Honkapuro, Samuli & Ohrling, Tiina, 2022. "Impact of home market on business development and internationalization of demand response firms," Energy, Elsevier, vol. 242(C).
    7. Laha, Priyanka & Chakraborty, Basab, 2021. "Low carbon electricity system for India in 2030 based on multi-objective multi-criteria assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Bishal Bharadwaj & Franzisca Weder & Peta Ashworth, 2023. "More support for hydrogen export than its domestic application in Australia," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-8, December.
    9. Arévalo, Paúl & Cano, Antonio & Jurado, Francisco, 2022. "Mitigation of carbon footprint with 100% renewable energy system by 2050: The case of Galapagos islands," Energy, Elsevier, vol. 245(C).
    10. Guglielmo D'Amico & Stefania Scocchera & Loriano Storchi, 2021. "Randentropy: a software to measure inequality in random systems," Papers 2103.09107, arXiv.org.
    11. Guevara-Luna, Marco Andrés & Madrazo, Jessie & Meneses, Elieza & Mora, Henry & Clappier, Alain, 2024. "Strategies toward an effective and sustainable energy transition for Cuba," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    12. D'Amico, Guglielmo & De Blasis, Riccardo, 2024. "Dividend based risk measures: A Markov chain approach," Applied Mathematics and Computation, Elsevier, vol. 471(C).
    13. Guglielmo D'Amico & Filippo Petroni & Philippe Regnault & Stefania Scocchera & Loriano Storchi, 2019. "A copula based Markov Reward approach to the credit spread in European Union," Papers 1902.00691, arXiv.org.
    14. Okonkwo, Eric C. & Wole-Osho, Ifeoluwa & Bamisile, Olusola & Abid, Muhammad & Al-Ansari, Tareq, 2021. "Grid integration of renewable energy in Qatar: Potentials and limitations," Energy, Elsevier, vol. 235(C).
    15. Guglielmo D’Amico & Philippe Regnault, 2018. "Dynamic Measurement of Poverty: Modeling and Estimation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 305-340, November.
    16. Riccardo De Blasis, 2020. "The price leadership share: a new measure of price discovery in financial markets," Annals of Finance, Springer, vol. 16(3), pages 381-405, September.
    17. Nikolaos Stavropoulos & Alexandra Papadopoulou & Pavlos Kolias, 2021. "Evaluating the Efficiency of Off-Ball Screens in Elite Basketball Teams via Second-Order Markov Modelling," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    18. D'Amico, Guglielmo & Di Biase, Giuseppe & Manca, Raimondo, 2014. "Decomposition Of The Population Dynamic Theil'S Entropy And Its Application To Four European Countries," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 55(2), pages 229-239, December.
    19. Mark Tocock & Dugald Tinch & Darla Hatton MacDonald & John M. Rose, 2023. "Managing the energy trilemma of reliability, affordability and renewables: Assessing consumer demands with discrete choice experiments," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(2), pages 155-175, April.
    20. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms," Applied Energy, Elsevier, vol. 316(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2184-:d:1387887. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.