t-ateya/renewable-energy-forecasting

# Renewable Energy Forecasting: Hybrid Deep Learning for Hydropower and Solar **Novel ensemble methodology combining STL decomposition, functional data analytics, and deep learning for Cameroon's energy grid** # Enhancing Renewable Energy Forecasting: A Hybrid Deep Learning Approach for Hydropower and Photovoltaic Systems _A Research Portfolio: Hybrid Deep Learning Framework for Renewable Energy Time Series Forecasting_ Abstract • Architecture • Key Features • Results • Citation • Contact --- ## Table of Contents 1. Abstract 2. Research Context 3. System Architecture 4. Key Features 5. Methodological Innovation 6. Implementation & Code Availability 7. Experimental Results - Hydropower Forecasting - Solar Irradiance Forecasting - Comparative Analysis 8. System Components 9. Evaluation Metrics 10. Reproducibility 11. Future Work 12. Research Collaboration 13. License 14. Citation 15. Acknowledgments 16. Contact 17. References --- ## Abstract Renewable energy sources—particularly hydropower and solar photovoltaic systems—have become cornerstone technologies in the global transition toward sustainable energy infrastructure. However, their inherent stochastic nature, characterized by unpredictable hydrological patterns and atmospheric variability, presents significant challenges for grid integration and operational planning. This research addresses these challenges through the development and validation of a novel hybrid deep learning framework that substantially improves forecasting accuracy for both energy sources. **Implementation Access:** The complete source code and implementation are available upon request for academic review. Please contact tateya@uco.edu with your academic affiliation and purpose of review. **For hydropower generation**, we developed an ensemble methodology incorporating Seasonal-Trend decomposition via Loess (STL), functional data analytics, and multiple deep learning architectures (Dense Neural Networks, 1D-CNNs, and LSTMs). Using 61,320 hourly observations from the Edea hydropower facility on Cameroon's Sanaga River (2010-2016), our Seasonal-Random ensemble configuration achieved supe …