Predicting and Explaining Hydropower Trends with Innovation Diffusion Models

Hydropower is an important renewable energy source that provides electricity and supports flood protection, irrigation, and access to clean water. This thesis analyses hydropower generation’s uptake and growth patterns in 43 countries, focusing on under- standing the factors driving these trends. Bass diffusion model is used to examine how innovation and imitation contribute to the diffusion of hydropower energy, while also assessing the model’s limitations in capturing adoption dynamics in resource-constrained or politically complex regions. To predict the future of this energy source, forecasting models such as ARIMA, Prophet, and the GGM were applied to the hydropower generation data for the period from 1965 to 2023. The analysis showed that the GGM consistently provided the most accurate predictions, outperforming both the Bass model and other methods. Regional differences were also evident, with some countries showing a greater reliance on social influence and imitation, while others showed innovation-driven growth. The study also used clustering techniques to categorise countries based on their characteristics in the adoption of hydropower, identifying two distinct patterns. Countries in the first cluster displayed a strong tendency towards innovation-driven growth. The second cluster relied significantly on social and community influences. Statistical validation of these clusters confirmed the robustness of the findings. Based on these findings, this research highlights the importance of tailoring strategies to specific regional conditions. Whether it involves fostering innovation, leveraging social influence, or improving infrastructure, hydropower adoption requires a nuanced approach. Recommendations include sustained investment, supportive policies, and enhanced regional cooperation to maximize the potential of hydropower in driving the global energy transition.