younesassini1998-ai/global-renewable-energy-forecasting-cnn-lstm

Official implementation of "A Hybrid CNN-LSTM Framework for Global Renewable Energy Forecasting integrating Socio-Economic Drivers, Explainability (SHAP), and a Case Study on Morocco (1990–2023)." # Global Renewable Energy Forecasting with Hybrid CNN-LSTM Official implementation of the paper: **"A Multi-Variable Hybrid CNN-LSTM Framework for Global Renewable Energy Forecasting: Integrating Socio-Economic Drivers, Explainability, and a Case Study on Morocco"** --- ## 📌 Abstract Accurate forecasting of renewable energy transition trajectories across heterogeneous global economies is critical for international energy planning. This repository provides a reproducible pipeline implementing a hybrid **CNN-LSTM** model on 33 years (1990–2023) of empirical data from *Our World in Data* across ~200 countries, augmented with macro-economic and demographic indicators. --- ## 🚀 Quick Start & Reproducibility ### 1. Installation ```bash git clone github.com cd global-renewable-energy-forecasting-cnn-lstm pip install -r requirements.txt