AI & IoT for Smart Energy Infrastructure: Predictive Analytics and Automation

📅 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.

🧰 Tools Covered: Wokwi, HiveMQ Cloud

📅 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.

🧰 Tools Covered: Google Colab

📅 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.

🧰 Tools Covered: Node-RED, HiveMQ Cloud, Adafruit IO

• 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.

• 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.