Asmienda/Hybrid_Eenery_AI

AI based hybrid energy prediction for inland vessels in Nigeria # AI-Powered Hybrid Energy Prediction for Inland Vessels This project uses machine learning to predict energy demand for inland vessels operating in Nigeria, with a focus on hybrid renewable energy (especially solar). The goal is to support sustainable energy planning for water transport in developing regions. # Why This Project? Inland waterway transport is growing in Nigeria, but it relies heavily on fuel based engines. This project explores AI-based forecasting for solar hybrid energy use on boats. It is aligned with my motivation for applying to the EMShip+ Erasmus Mundus program, combining computer engineering, sustainability, and marine technology. # Tools & Technologies Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn) Jupyter Notebook Machine Learning: Linear Regression, Random Forest Energy Data: Simulated + temperature and solar radiation data from Lokoja/Onitsha Feature Engineering: Load weight, distance, solar radiation, and temperature ## Model Performance Model R² Score MSE Basic Linear Model -0.19 6.82 Improved Model 0.92 0.48 Best model: Random Forest with engineered features.