📅 Day 1 – Energy Forecasting with Machine Learning
- Apply regression models and deep learning techniques for solar and wind energy forecasting.
- Hands-on: Developing forecasting models for solar/wind energy using historical data and machine learning algorithms (Google Colab).
- Time-Series Analysis for Renewable Energy: Implement time-series models like ARIMA and LSTM for predicting energy production.
- Hands-on: Building time-series models for renewable energy forecasting (Jupyter Notebook).
👉 Outcome: Solar/wind energy forecasting models + .ipynb.
📅 Day 2 – Optimization of Smart Grids & Microgrids
- Utilize AI for optimizing the operation and energy distribution in smart grids and microgrids.
- Hands-on: Implementing optimization algorithms for energy flow in microgrids and smart grids (Google Colab).
- AI for Energy Distribution and Management: Integrating renewable energy sources into decentralized systems using machine learning and optimization techniques.
- Hands-on: Energy management optimization in decentralized energy systems (Jupyter Notebook).
👉 Outcome: Optimized energy management models for smart grids + .ipynb.
📅 Day 3 – Resilience Modeling for Smart Grids & Climate-Resilient Energy Systems
- Apply AI to increase the resilience of energy systems in the face of climate change and other external factors.
- Hands-on: Modeling resilience in smart grid systems to enhance reliability under various scenarios (Google Colab).
- AI for Climate-Resilient Energy Systems: Develop models that adapt energy distribution to climate-related challenges.
- Hands-on: Building climate-resilient energy systems using AI tools (Jupyter Notebook).
👉 Outcome: Resilience models for climate-adaptive energy systems + .ipynb.
