About Workshop
The workshop explores how Artificial Intelligence, IoT sensors, smart monitoring systems, predictive analytics, and automated control mechanisms can be used to reduce energy waste, optimize building operations, support smart grids, and enable sustainable infrastructure planning. Participants will learn how real-time data from connected devices can be analyzed using AI models to improve energy forecasting, fault detection, load management, and system-level decision-making.
Aim
What Participants Will Learn
Structure
📅 Day 1: Foundations of Smart Infrastructure, IoT Sensing & Energy Data Connectivity
- Importance of Energy Efficiency in Smart and Sustainable Infrastructure
- Basics of Smart Buildings, Green Infrastructure, and Intelligent Energy Monitoring
- Introduction to IoT Architecture for Energy-Management Systems
- Role of ESP32, Virtual Sensors, and Smart Meters in Infrastructure Monitoring
- Real-Time Energy Telemetry and Sensor Data Communication
- Introduction to MQTT Protocol and Cloud-Based IoT Data Publishing
- How IoT Systems Collect, Transmit, and Monitor Energy Consumption Data
🛠️ Hands-on:
- Hands-on 1: Build a virtual smart meter using ESP32 and simulated energy sensors to understand how load data is captured and converted into useful telemetry.
- Hands-on 2: Publish simulated smart-meter readings to an MQTT broker and learn how real-time energy data moves from IoT devices to cloud-based monitoring systems.
📅 Day 2: AI-Based Energy Intelligence & Predictive Consumption Analysis
- Importance of AI in Reducing Energy Waste and Improving Infrastructure Efficiency
- Understanding Energy Usage Patterns, Peak Demand, and Operational Inefficiencies
- Basics of Data Preprocessing for Energy Consumption Datasets
- Predictive Modeling for HVAC, Cooling Load, and Energy Demand Forecasting
- Regression Analysis for Estimating Future Cooling or Power Requirements
- Anomaly Detection for Identifying Abnormal or Wasteful Device Behavior
- Clustering Techniques for Detecting Inefficient or Continuously Consuming Devices
🛠️ Hands-on:
- Hands-on 1: Train a basic regression model to forecast HVAC cooling requirements using simulated weather and energy-consumption data.
- Hands-on 2: Apply clustering-based analysis to identify abnormal energy usage, inefficient devices, and possible energy-wasting loads.
📅 Day 3: Automated Control, IoT Integration & Green Infrastructure Dashboarding
- Importance of Feedback Loops in Intelligent Energy-Management Systems
- Automated Actuation for Reducing Power Consumption During Peak Demand
- Edge Logic for Smart Control of Lights, HVAC Systems, and Non-Essential Loads
- Integration of IoT Devices, MQTT Streams, and Automation Workflows
- Designing Centralized Dashboards for Energy Monitoring and Decision-Making
- Remote Override, Alerting, and Visualization for Smart Infrastructure Systems
- Security, Scalability, and Deployment Challenges in AI-IoT Green Technology Systems
🛠️ Hands-on:
- Hands-on 1: Create edge automation logic to reduce non-essential loads during peak pricing or high-demand conditions.
- Hands-on 2: Design a live green infrastructure dashboard to monitor energy consumption, device status, and infrastructure performance.
Important Dates
Registration Ends
4:30 PM
Workshop Dates
2026-06-04
05:30 PM
05:30 PM
What You Will Gain
Outcomes
- Gain practical understanding of AI-IoT integration for energy efficiency.
- Learn to use data for energy monitoring and optimization.
- Understand applications in smart buildings, grids, and sustainable infrastructure.
- Identify opportunities for reducing energy waste and operational costs.
- Build readiness for projects in green technology and climate-smart systems.
Who Should Attend
- Open to students, researchers, faculty, engineers, and working professionals.
- Suitable for learners from engineering, energy, sustainability, IoT, AI/ML, and smart infrastructure domains.
- Basic understanding of technology or energy systems is helpful but not mandatory.
- No prior coding or IoT hardware experience is required.