Version 1
: Received: 7 October 2024 / Approved: 7 October 2024 / Online: 8 October 2024 (11:23:44 CEST)
How to cite:
Ahmad, F.; Finos, L.; Guidolin, M. Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis. Preprints2024, 2024100526. https://doi.org/10.20944/preprints202410.0526.v1
Ahmad, F.; Finos, L.; Guidolin, M. Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis. Preprints 2024, 2024100526. https://doi.org/10.20944/preprints202410.0526.v1
Ahmad, F.; Finos, L.; Guidolin, M. Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis. Preprints2024, 2024100526. https://doi.org/10.20944/preprints202410.0526.v1
APA Style
Ahmad, F., Finos, L., & Guidolin, M. (2024). Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis. Preprints. https://doi.org/10.20944/preprints202410.0526.v1
Chicago/Turabian Style
Ahmad, F., Livio Finos and Mariangela Guidolin. 2024 "Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis" Preprints. https://doi.org/10.20944/preprints202410.0526.v1
Abstract
Hydroelectric power is one of the most essential renewable energy sources in the world. In addition to generating electricity, hydropower offers other benefits such as flood control, irrigation assistance, and clean drinking water. In this study, we examine the evolution of hydropower in the context of energy transition with a forecasting analysis. We analyze 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 model outperforms the Prophet and ARIMA models, and is more accurate than the Bass Model. This analysis underscores the critical role of precise forecasting in energy planning and suggests further research to validate these results and explore other factors influencing the development of hydroelectric generation.
Keywords
Energy transition; hydropower; forecasting; Guseo and Guidolin Model (GGM); Bass model (BM); ARIMA model; Prophet model
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
