🌱 LCA & CO₂ Dashboards for Smart Energy Systems

About This Course

“LCA & CO₂ Dashboards for Smart Energy Systems” is a comprehensive 3-week course that blends environmental analytics, smart grid intelligence, and data visualization to empower energy professionals to quantify and communicate the environmental footprint of energy infrastructure, devices, and consumption patterns.

In this course, participants will work with open Life Cycle Assessment (LCA) datasets, simulation results, and real-time IoT/SCADA-based emission streams. They will learn to use tools like Python (Plotly, Streamlit), Power BI, OpenLCA, and Tableau to build dashboards that integrate life cycle stages, carbon factors, and energy metrics for power plants, microgrids, and renewable energy systems.

Aim

This course aims to equip participants with the practical tools and strategies to design, develop, and interpret LCA and carbon dashboards. These dashboards will track environmental impacts and CO₂ emissions across smart energy systems, supporting data-driven decisions to meet net-zero goals.

Course Objectives

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

• Enable hands-on creation of carbon dashboards using 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

Course Structure

📅 Module 1: Machine Learning for Renewable Integration

Theme: Integrating Renewables into Energy Systems

• Introduction to Load Forecasting

  • Basics of energy demand and load profiles

  • Challenges in forecasting with renewable energy sources

• Machine Learning Models

  • Time-series forecasting techniques: ARIMA, LSTM, and more

  • Feature engineering and data preprocessing

Hands-On Lab:

• Solar and wind integration forecasting projects

• Python walkthrough: Forecasting electricity demand and energy usage patterns

📅 Module 2: Reinforcement Learning for Real-Time Demand Response

Theme: Applying Reinforcement Learning for Dynamic Grid Balancing

• Introduction to Reinforcement Learning (RL)

  • Key concepts: Agent, environment, reward, policy

  • Differences between RL and supervised learning in energy systems

• RL for Demand Response Applications

  • Real-time grid balancing with Demand Response (DR) programs

  • Dynamic pricing and optimizing energy flexibility

Hands-On Lab:

• Using OpenAI Gym and grid simulators to deploy RL models for household or commercial DR control

• Simulating demand-response scenarios

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

Theme: Tracking Environmental Impact and Building Dashboards

• Fundamentals of LCA

  • Cradle-to-grave assessment of energy systems

  • Key metrics: GWP (Global Warming Potential), energy payback time

• CO₂ Dashboards for Monitoring Impact

  • Tools and platforms for CO₂ tracking and visualization

  • Dashboard design using Power BI, Streamlit, and other platforms

Hands-On Lab:

• Create your own carbon impact dashboard

• Use cases: Smart buildings, Electric Vehicles (EVs), microgrids, and more

Who Should Enrol?

• Energy engineers and sustainability managers

• ESG analysts and carbon auditors

• Data scientists working in climate-tech

• Urban planners and green infrastructure consultants

• UG/PG/PhD students in energy, environmental science, or data analytics