AI Compliance in Energy
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Course Overview
Table of Contents
Program Syllabus
Module 1
About
The AI Compliance in Energy program is a 6-week interdisciplinary training that equips participants with the knowledge and tools to align AI applications in the energy sector with global regulatory standards. Covering carbon accounting, smart grids, cybersecurity, and market integrity, the course emphasizes compliance, transparency, and responsible AI use. Participants will explore frameworks like the EU AI Act, ISO/IEC 42001, CSRD, and NERC CIP, and apply these through real-world case studies and hands-on project work.
Module 2
Aim
The aim of the AI Compliance in Energy course is to equip participants with the critical knowledge and practical skills required to develop, deploy, and manage AI systems in the energy sector that are ethically sound, legally compliant, and operationally resilient. The program focuses on aligning AI innovations with global regulatory frameworks, industry standards, and governance protocols to ensure transparency, accountability, cybersecurity, and sustainability in AI-driven energy infrastructures and markets.
Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
AI Compliance in Energy
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Module 3
Program Structure
Module 4
1.1 Understanding AI in the Energy Ecosystem
- Definition and scope of AI in power systems, utilities, and grid operations
- Examples of AI use in generation, distribution, demand response, DERs
Module 5
1.2 Global Regulatory Landscape
- EU AI Act: objectives, classifications, and impact on energy applications
- Overview of U.S., China, India, and other national AI regulations in energy
- High-risk vs. unacceptable-risk AI use-cases
Module 6
1.3 Industry-Specific Risks & Compliance Pressure
- Mapping AI risks to energy sector operations (generation, retail, trading)
- Policy pressure from ESG goals, net-zero mandates, and AI oversight
Module 7
2.1 ISO/IEC 42001 for AI Management in Utilities
- What is ISO/IEC 42001?
- Implementing ISO 42001 in the utility industry
- Intersection with ISO 27001 (cybersecurity) and ISO 9001 (quality management)
Module 8
2.2 Data Governance in Energy AI Systems
- Principles of data privacy, integrity, lineage in smart-meter ecosystems
- GDPR, data anonymization, consent management for AI-driven use
- Challenges in cross-border smart energy data processing
Module 9
2.3 Building Trustworthy AI
- Explainability, fairness, transparency principles (per OECD, NIST)
- Tools for ethical AI model validation and documentation in energy
Module 10
3.1 AI-Driven Grid Operations
- Forecasting, load balancing, anomaly detection
- Real-time vs. batch AI in grid management
Module 11
3.2 Explainability and Audit Trails
- Explainable AI (XAI) in energy: SHAP, LIME, counterfactuals
- Building traceable audit logs for ML models
- Regulatory expectations for traceability and accountability
Module 12
3.3 AI Lifecycle Compliance
- MLOps best practices in compliance-sensitive environments
- Monitoring, drift detection, model versioning aligned with policy
Module 13
4.1 Regulatory Drivers
- CSRD (EU), SEC climate rules (US), and national disclosures
- Impact of AI use on carbon footprint traceability and emissions
Module 14
4.2 AI for GHG Tracking and Reporting
- Automating Scope 1–3 emissions tracking with AI
- AI model design for emissions forecasting
- Ethics of estimation and proxy modeling in regulatory reports
Module 15
4.3 ISO 14064-2 Integration
- Understanding the standard’s scope and purpose
- Measuring and verifying AI-enabled emissions reductions
- Challenges in quantification and audit readiness
Module 16
5.1 NERC CIP and OT-IT Integration
- Overview of NERC CIP controls for critical infrastructure
- Importance of cybersecurity in AI-powered SCADA, EMS systems
Module 17
5.2 AI Overlay in Cybersecurity
- AI models for anomaly, intrusion, and threat detection
- Model vulnerabilities and adversarial AI in the energy sector
Module 18
5.3 AI-Specific Cyber-Physical Risks
- Cyber-physical convergence risks in AI-controlled systems
- Regulatory red flags and compliance gaps
Module 19
6.1 Market Trading and AI Applications
- Real-time bidding agents, price forecasting models
- Algorithmic trading and auction platforms
Module 20
6.2 Collusion Detection and Market Fairness
- Regulatory expectations from market integrity
- How AI can enable or prevent anti-competitive behavior
- Detecting bid-rigging and unfair optimizations
Module 21
6.3 Transparency in Market Algorithms
- Black-box vs white-box models in bidding
- Disclosure requirements for regulators and market operators
Module 22
7.1 AI-Enabled DERMS and Grid Resilience
- Role of Distributed Energy Resource Management Systems
- AI in voltage control, storage dispatch, and islanding
Module 23
7.2 Incident Response Planning
- Developing playbooks for AI failures, cybersecurity breaches
- Lessons from past grid and AI system failures
Module 24
7.3 Legal and Liability Framework
- Accountability in AI-driven decisions
- Chain of responsibility in DERMS incidents
- Insurance and compliance recordkeeping
Module 25
8.1 Simulation and Capstone Project
- Design and assess a compliant AI-based energy system
- Create documentation: audit logs, explainability reports, governance plan
Module 26
8.2 Case Studies and Guest Lectures
- Real-world failures and compliance breaches (e.g., Texas grid, Europe)
- ISO 42001 success in utility AI deployments
- SEC enforcement and AI fraud detection in carbon markets
Module 27
8.3 Final Evaluation & Certification
Module 28
Intended For
Module 29
Target Audience
- PhD Scholars & Postgraduate Students in AI, energy systems, environmental sciences, or regulatory studies.
- Academic Faculty & Researchers working on energy technologies, smart grids, or AI governance.
- Industry Professionals from utilities, renewable energy, smart-grid operations, AI/ML development, compliance, and sustainability teams.
- Policy Analysts & Regulatory Advisors shaping or interpreting AI and energy-related policies.
- Technology Developers & Engineers deploying AI in energy infrastructure who must meet legal and ethical standards.
Module 31
Fee Structure
Fee: INR 16998 USD 224
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of CurrenciesModule 32
FOR QUERIES, FEEDBACK OR ASSISTANCE
Module 33
Key Takeaways
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Module 34
Enter the Hall of Fame!
Take your research to the next level!
Publication Opportunity
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Centre of Excellence
Join the esteemed Centre of Excellence.
Networking and Learning
Network with industry leaders, access ongoing learning opportunities.
Hall of Fame
Get your groundbreaking work considered for publication in a prestigious Open Access Journal (worth ₹20,000/USD 1,000).
Achieve excellence and solidify your reputation among the elite!
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Module 35
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