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Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis

Author

Listed:
  • Farooq Ahmad

    (Department of Statistical Sciences, University of Padua, 35121 Padua, Italy)

  • Livio Finos

    (Department of Statistical Sciences, University of Padua, 35121 Padua, Italy)

  • Mariangela Guidolin

    (Department of Statistical Sciences, University of Padua, 35121 Padua, Italy)

Abstract

Hydroelectric power is one of the most important renewable energy sources in the world. It currently generates more electricity than all other renewable technologies combined and, according to the International Energy Agency, it is expected to remain the world’s largest source of renewable electricity generation into the 2030s. Thus, despite the increasing focus on more recent energy technologies, such as solar and wind power, it will continue to play a critical role in energy transition. The management of hydropower plants and future planning should be ensured through careful planning based on the suitable forecasting of the future of this energy source. Starting from these considerations, in this paper, we examine the evolution of hydropower with a forecasting analysis for a selected group of countries. We analyze the time-series data of hydropower generation from 1965 to 2023 and apply Innovation Diffusion Models, as well as other models such as Prophet and ARIMA, for comparison. The models are evaluated for different geographical regions, namely the North, South, and Central American countries, the European countries, and the Middle East with Asian countries, to determine their effectiveness in predicting trends in hydropower generation. The models’ accuracy is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Through this analysis, we find that, on average, the GGM outperforms the Prophet and ARIMA models, and is more accurate than the Bass model. This study underscores the critical role of precise forecasting in energy planning and suggests further research to validate these results and explore other factors influencing the future of hydroelectric generation.

Suggested Citation

  • Farooq Ahmad & Livio Finos & Mariangela Guidolin, 2024. "Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis," Forecasting, MDPI, vol. 6(4), pages 1-20, November.
    • Handle: RePEc:gam:jforec:v:6:y:2024:i:4:p:52-1064:d:1522285
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    References listed on IDEAS

    as
    1. Junbing Huang & Yuee Tang & Shuxing Chen, 2018. "Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-13, January.
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    4. Herrera, Gabriel Paes & Constantino, Michel & Tabak, Benjamin Miranda & Pistori, Hemerson & Su, Jen-Je & Naranpanawa, Athula, 2019. "Long-term forecast of energy commodities price using machine learning," Energy, Elsevier, vol. 179(C), pages 214-221.
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