Statistics, Probability and Uncertainty, Statistics, Probability and Uncertainty, Visual Arts and Performing Arts
3
Scopus Publications
Scopus Publications
Understanding Hydropower Generation Across Countries Through Innovation Diffusion Models Farooq Ahmad, Mariangela Guidolin Energies, 2026 The world is increasingly confronted with interconnected challenges such as energy shortages and climate change. Fossil fuels, including coal, oil, and natural gas, remain the dominant global energy sources, yet they are major contributors to greenhouse gas emissions and growing geopolitical instability. In response to energy insecurity and environmental pressures, many countries are expanding their use of renewable energy sources, including hydropower, solar, wind, and geothermal. Hydropower currently generates more electricity than all other renewable technologies combined and is expected to remain the largest source of renewable electricity through the 2030s. This paper analyzes the role of hydropower in national energy transitions by applying innovation diffusion models. Using an innovation diffusion framework, via the Bass Model, we examine the dynamics of hydropower generation across multiple countries and find that this approach effectively captures the mean nonlinear trajectory of most countries. We complete the analysis by evaluating the effect of rainfall on hydropower generation and show that this helps capture the residual variability not modeled by the Bass Model.
Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis Farooq Ahmad, Livio Finos, Mariangela Guidolin Forecasting, 2024 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.
