About Workshop
Digital Twins for Climate-Resilient Sustainability: AI, Simulation and Dashboard-Based Decision Intelligence is a 3-day hands-on workshop designed to introduce participants to the practical use of digital twin concepts, AI-based analysis, climate-impact simulation, and dashboard development for sustainability decision-making.
Aim
The aim of this workshop is to help participants understand how AI, digital twins, simulation, and dashboards can be used to support climate-resilient sustainability planning for buildings, campuses, industrial facilities, and urban infrastructure.
What Participants Will Learn
- Introduce the concept of digital twins for sustainable buildings, campuses, industrial systems, and urban facilities.
- Explain how AI supports climate-resilient infrastructure planning and sustainability analytics.
- Help participants identify key sustainability indicators such as energy use, carbon impact, water consumption, resource efficiency, and operational performance.
- Build understanding of climate-risk indicators such as heat stress, flooding risk, air quality, extreme weather, and infrastructure disruption.
- Demonstrate how simulation tools can be used for climate-impact and resource-flow analysis.
- Enable participants to perform basic sustainability scenario analysis using Python and Google Colab.
- Introduce QGIS-based spatial visualization for climate-risk and sustainability mapping.
- Guide participants in creating dashboard indicators for energy demand, carbon impact, resilience score, climate risk, and operational efficiency.
Structure
📅 Day 1: Foundations of Digital Twins and Climate-Resilient Sustainability
- Introduction to digital twins for sustainable buildings, campuses, and industrial systems
- Understanding the role of AI in climate-resilient infrastructure planning
- Concept of physical system, virtual model, data layer, and decision layer
- Key sustainability indicators: energy use, carbon impact, water use, resource efficiency, and operational performance
- Climate-risk indicators: heat stress, flooding risk, air quality, extreme weather, and infrastructure disruption
- How digital twins support monitoring, simulation, prediction, and decision-making
- Developing a digital twin framework for a building, campus, or industrial facility
- Introduction to simulation-based sustainability planning
- Understanding system behavior through resource flows, occupancy, energy demand, and operational load
- Basics of discrete-event simulation using SimPy / AnyLogic
- Using Python for climate and sustainability data analysis
- Scenario modeling for heatwaves, energy demand, flooding disruption, or operational stress
- AI-assisted pattern detection for climate risk and system performance
- Interpreting simulation results for sustainability and resilience decisions
- Converting digital twin and simulation outputs into decision-ready insights
- Designing dashboard indicators: energy demand, carbon impact, resilience score, climate risk, and operational efficiency
- Using QGIS for spatial climate-risk and sustainability visualization
- Scenario comparison: baseline system vs climate-resilient intervention
- Creating decision dashboards for buildings, campuses, industrial systems, and urban facilities
- Using dashboards for ESG reporting, infrastructure planning, facility management, and sustainability communication
- Developing a final digital twin concept model for research or professional application
Important Dates
Registration Ends
4:30 PM IST
Workshop Dates
2026-06-09
5:30 IST
5:30 IST
What You Will Gain
Outcomes
- Explain the role of digital twins in climate-resilient sustainability planning.
- Identify physical system components, virtual model components, data layers, and decision layers in a digital twin framework.
- Prepare a sustainability and climate-risk indicator matrix for a building, campus, or industrial facility.
- Use Python and Google Colab for basic climate and sustainability data analysis.
- Understand how simulation can support sustainability planning and climate-impact assessment.
- Build basic climate-impact scenarios such as heatwave stress, energy-demand variation, flooding disruption, or operational load changes.
- Interpret simulation outputs for resilience and sustainability decision-making.
- Use QGIS for spatial climate-risk and sustainability visualization.
Who Should Attend
- Students and postgraduate learners in engineering, environmental science, sustainability, data science, urban planning, and related fields
- PhD scholars and researchers working in sustainability, climate resilience, smart infrastructure, ESG, energy systems, and environmental analytics
- Academicians and faculty members interested in AI-enabled sustainability education and research
- Sustainability professionals, ESG analysts, and net-zero planning professionals
- Civil, environmental, energy, industrial, and infrastructure engineers
- Urban planners, facility managers, and smart city professionals
- Data science and AI learners interested in real-world sustainability and climate-risk applications