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Enron Egg: AI & IoT-Powered Smart Energy Storage for a Sustainable Future

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Delivery Mode
Virtual / Online
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Level
Moderate
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Duration
3 Days( 60-90 Minutes each day )
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Certificate
Mentor Based
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Language
English
Rating
4 Stars
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About Workshop

The Enron Egg is a conceptual next-generation energy storage device designed for smart grids, renewable energy optimization, and sustainable infrastructure. This 3-day workshop offers participants a research-oriented, hands-on experience in AI-powered energy modeling, IoT-based monitoring, and virtual prototyping. Participants will gain insights into future-ready energy technologies, learn how to simulate and optimize energy storage, and explore global applications in homes, industries, and microgrids.
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Aim

To equip participants with hands-on knowledge and practical skills in designing, simulating, and optimizing smart energy storage solutions using AI and IoT, with a focus on renewable energy integration and sustainable infrastructure.
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What Participants Will Learn

  • Understand the challenges and solutions in modern energy systems, including renewable integration and smart grids.
  • Learn the conceptual design and functionalities of the Enron Egg as a compact, AI-IoT-enabled energy storage solution.
  • Develop hands-on skills in AI-driven predictive load balancing and energy optimization using IoT data.
  • Simulate energy storage, renewable energy integration, and load management in virtual prototyping environments.
  • Explore career and research opportunities in smart energy, AI-IoT systems, and sustainable infrastructure.
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Structure

📅 Day 1: Foundations of Smart Energy & Enron Egg Concept

  • Global energy challenges and renewable adoption trends
  • Introduction to energy storage technologies and smart grids
  • Conceptual overview of the Enron Egg – AI & IoT-enabled compact energy storage
  • Basics of IoT integration and AI-driven energy monitoring

🛠️ Hands-on:

  • Simulate energy consumption using virtual IoT sensors
  • Build a simple energy monitoring dashboard for a microgrid

📅 Day 2: AI & IoT for Energy Optimization

  • IoT-enabled real-time energy monitoring and control
  • AI-driven predictive load balancing and energy demand forecasting
  • Modeling energy storage and renewable integration for optimization
  • Strategies to improve energy efficiency in homes and industries

🛠️ Hands-on:

  • Create an AI-IoT energy optimization model using Python & Google Colab
  • Integrate virtual IoT data to optimize load distribution

📅 Day 3: Prototype Simulation & Future Applications

  • Conceptual Enron Egg prototype: modularity, connectivity, and energy efficiency
  • Simulate renewable energy integration and load management
  • Evaluate environmental impact: carbon reduction and net-zero initiatives
  • Career pathways, research opportunities, and entrepreneurial prospects in AI-IoT energy

🛠️ Hands-on:

  • Virtual prototype: energy storage with renewable integration
  • Case study: optimize energy distribution for efficiency and sustainability

🧰 Tools & Platforms Covered:

  • IoT & Simulation: Wokwi (Arduino/Raspberry Pi), Node-RED, Adafruit IO
  • AI & Analytics: Python, Google Colab, NumPy, Pandas, Scikit-learn
  • Visualization & Dashboards: IoT dashboards, Matplotlib, Plotly
  • Resources: Cloud-based simulation environments or local systems

Important Dates

Registration Ends

04: 00 PM

Workshop Dates

2026-06-02
5:00PM
5:00PM
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What You Will Gain

Sample Certificate
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Outcomes

  • Practical experience with AI-IoT energy modeling and simulations
  • Understanding of smart grid integration and renewable energy optimization
  • Knowledge to design conceptual energy storage solutions like the Enron Egg
  • Ability to analyze energy efficiency, reduce wastage, and simulate sustainable infrastructure
  • Global-ready skills for research, industry, or entrepreneurship in the energy and sustainability sector
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Who Should Attend

  • Engineers, Researchers, Sustainability Professionals, Students, and Academicians
  • Basic understanding of energy systems, IoT, or Python programming is helpful but not mandatory
  • No prior exposure to AI-IoT energy devices is required; all foundational concepts will be covered
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