Lilly Tech Systems
AI in Energy & Climate
Explore how artificial intelligence is accelerating the clean energy transition — from forecasting renewable generation and optimizing power grids to tracking carbon emissions and modeling climate change.
Your Learning Path
Follow these lessons to understand how AI is powering the transition to a sustainable energy future.
1. Introduction
AI in energy and climate overview: the decarbonization challenge, key applications, and industry landscape.
2. Energy Forecasting
ML models for solar, wind, and demand forecasting that enable reliable renewable energy integration.
3. Grid Optimization
AI for smart grid management, demand response, energy storage, and distributed resource coordination.
4. Carbon Tracking
AI-powered emissions monitoring, carbon accounting, satellite-based verification, and ESG reporting.
5. Climate Modeling
Machine learning for weather prediction, climate projections, extreme event forecasting, and adaptation planning.
6. Best Practices
AI's own carbon footprint, responsible deployment, policy considerations, and scaling climate AI solutions.
What You'll Learn
By the end of this course, you'll understand:
Energy Forecasting
How ML predicts solar and wind generation to integrate renewables reliably into the grid.
Smart Grids
How AI optimizes power distribution, balances supply and demand, and manages energy storage.
Carbon Intelligence
How AI monitors, verifies, and reduces greenhouse gas emissions across industries.
Climate Science
How ML accelerates climate modeling, improves weather forecasts, and informs adaptation strategies.