Program Objectives:

• Introduce participants to LCA as a tool for sustainable energy planning

• Enable hands-on creation of carbon dashboards with real or simulated data

• Promote transparency and traceability in emission metrics

• Support green reporting, regulatory alignment, and net-zero initiatives

• Foster a data-driven mindset in smart grid design and energy systems planning

What you will learn?

Day 1: Machine Learning for Renewable Integration

• Introduction to Load Forecasting

  • Basics of energy demand and load profiles

  • Challenges in forecasting with renewables

• Machine Learning Models

  • Time-series forecasting techniques (ARIMA, LSTM, etc.)

  • Feature engineering and data preprocessing

• Case Studies & Hands-on

  • Solar and wind integration forecasting projects

  • Python walkthrough: Forecasting electricity demand

Day 2: Reinforcement Learning for Real-Time Demand Response

• Introduction to Reinforcement Learning (RL)

  • Key concepts: Agent, environment, reward, policy

  • RL vs. supervised learning in energy systems

• RL for Demand Response Applications

  • Real-time grid balancing with DR programs

  • Dynamic pricing and energy flexibility

• Simulations & Deployment

  • Hands-on with OpenAI Gym & Grid simulators

  • RL models for household or commercial DR control

Day 3: Life Cycle Assessment (LCA) & CO₂-Impact Dashboards

• Fundamentals of LCA

  • Cradle-to-grave assessment of energy systems

  • Key metrics: GWP, energy payback time

• CO₂ Dashboards for Monitoring Impact

  • Tools & platforms for CO₂ tracking

  • Dashboard design and visualization (Power BI, Streamlit)

• Practical Implementation

  • Create your own carbon impact dashboard

  • Use cases: Smart buildings, EVs, and microgrids