Course Content
Lesson 1: The History of AI and its rise
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What is AI?
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Surging: Key breakthroughs that caused the recent surge
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How do we use AI?
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Lesson 1 – The History of AI and Its Rise
Lesson 2 – Key AI Concepts & Vocabulary
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Definitions and relationships: AI vs Machine Learning (ML) vs Deep Learning (DL), Digital Twin (DT)
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Narrow AI vs General AI
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Rules-based systems vs learning systems
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Tasks vs data vs models
Lesson 3: AI Archetypes
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The Three archetypes
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Types of AI
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Lesson 4: Data: The Fuel of AI
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What is data? Structured vs unstructured; time series, images, text
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Features and labels – what they mean in practice
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Data quality: noise, missing values, bias, representativeness
The data lifecycle: collection → cleaning → labeling → storage → governance
Basic data challenges in the energy sector (sensor errors, sparse data, rare events like outages)
Lesson 5 : How AI Learning Works
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A Model is just a function
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The Training Loop: Learn by Repetition
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Loss Function: Measuring How Wrong the Model Is
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Overfitting, Generalisation & Train/Validation/Test Sets
More Data & Compute: Powerful, but Not Magic
Lesson 06 – Modern AI & Generative Models
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Discriminative vs Generative: Predict or Create?
Large Language Models: Predicting the Next Word
Beyond Text: Generating Images, Audio, and Code
Foundation Models vs Narrow Models
Fine-Tuning & Prompting: Adapting Foundation Models
Why Generative AI Matters for Energy Sector
Lesson 07 – The Backbone of AI
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From Sand to Smart Grids: The AI Supply Chain
What Do We Mean by AI Infrastructure?
The Chips That Power AI
The AI Software Stack
Data Centres: Where AI Lives
From Sensors to Smart Decisions: AI in the Energy Supply Chain
Lesson 08 – AI Capabilities and Performance
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AI vs Humans: Narrow Superpowers, Broad Limitation
How Accurate Are AI Models?
Understanding AI Performance Metrics
Benchmarks: Useful, but Not the Whole Story
AI and Humans: Complementary Strengths
Lesson 09 – AI Energy Consumption
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Step 1: Making the Hardware (Embodied Energy)
Step 2: Training Models (One-off but Heavy)
Step 3: Using Models (Inference Energy)
How Do We Reduce AI’s Energy Footprint?
Performance vs Energy: Choosing the Right Balance
Lesson 10 – Responsible & Safe AI in the Energy Sector
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Fairness & Bias: Who Benefits, Who Is Left Out?
Transparency & Explainability: Making AI Understandable
Privacy & Security: Protecting Data and Systems
Human-in-the-Loop: AI as Assistant, Humans as Decision-Makers
AI Regulation & Policy: Fast-Moving and Energy-Relevant
Lesson 11 – How AI Projects Happen in the Energy Sector
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Step 1: From ‘Let’s Use AI’ to a Clear Use Case”
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Step 2: Data Reality Check
Step 3: Designing the Solution – Who Does What?
Step 4: Pilot, Deploy, and Integrate
Step 5: Monitoring, Governance, and Iteration
Real Example: AI Project Lifecycle – Wind Turbine Predictive Maintenance
Essentials of AI for Energy Sector Professionals
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