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
Hands-on 1: Digital Twin System Mapping for a Building / Campus / Industrial FacilityHands-on 2: Climate-Risk and Sustainability Indicator Matrix Preparation📅 Day 2: AI and Simulation for Climate Impact Analysis
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
Hands-on 1: Basic Climate-Impact Simulation Using SimPy / AnyLogicHands-on 2: AI-Assisted Sustainability Scenario Analysis in Python📅 Day 3: Climate-Resilience Dashboards and Decision Intelligence
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
Hands-on 1: Climate-Resilience Dashboard Creation in Power BIHands-on 2: Mini Project: Digital Twin-Based Climate Scenario and Sustainability Decision Dashboard🧰 Tools Covered: Power BI, QGIS, Python, Google Colab, SimPy / AnyLogic
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
