EE400 — AI and Machine Learning Fundamentals for Energy

Where AI Meets the Grid

EE400 is the entry point of GIEE's flagship AI in Energy curriculum. This foundational course takes engineers from "I've heard about AI" to "I can apply machine learning methods to grid and energy problems."

Every concept is taught through real grid and energy examples. You will learn overfitting through a load forecasting failure. You will learn random forests through a transformer health prediction case study. You will learn cross-validation through a time-series forecast that fell apart in 2021. The pedagogy is grounded in domain expertise from utility operations and DER planning.

EE400 is required for both CAGP (Certified AI for Grid Professional) and CAEP (Certified AI for Energy Professional) certifications, and is the foundation that makes every subsequent course in the curriculum possible.

What You Will Learn

• Define artificial intelligence and explain its core capabilities through real grid and energy examples
• Distinguish supervised, unsupervised, and reinforcement learning with applied use cases for each
• Apply core machine learning algorithms (linear regression, decision trees, random forests, XGBoost) to grid and energy data
• Engineer features from raw energy data including weather variables, time features, and lag features for load forecasting
• Evaluate model quality using appropriate metrics (MAE, RMSE, MAPE, precision, recall, F1, AUC-ROC)
• Recognize overfitting, underfitting, and the bias-variance tradeoff in engineering contexts
• Apply cross-validation techniques appropriate to time-series and energy data
• Choose the right algorithm based on problem characteristics, data availability, and interpretability requirements

Course Structure

EE400 is organized into four modules, each approximately three hours of content, plus a comprehensive final exam:

• Module 1: AI Fundamentals — What AI is, its history and evolution, types of AI, and core vocabulary every energy professional needs to know.
• Module 2: Data Foundations for AI — Data sources in energy, data quality, preprocessing, and feature engineering for machine learning.
• Module 3: Machine Learning — The three families of ML, the most important algorithms, and applied algorithm selection for engineering problems.
• Module 4: Model Evaluation and Validation — How to know whether a machine learning model is actually working, with metrics and validation techniques tailored to energy data.

Real-World Examples

Every lesson uses real examples from utilities and energy companies including Eversource, Duke Energy, ERCOT, ISO-NE, National Grid, PG&E, Dominion, Xcel, and AES. You will see actual MAPE numbers, real algorithm comparisons, and quantified financial impacts. No generic "AI in business" examples; every case study is grounded in energy.

Who This Course Is For

• Practicing engineers in the energy sector who need to understand AI capabilities and limitations
• Recent engineering graduates building expertise for energy AI roles
• Career transitioners moving into the energy AI space
• Anyone pursuing CAGP or CAEP certification
• Engineering managers and leads evaluating AI projects and vendors

Prerequisites

• Engineering or technical background (any discipline)
• Basic mathematics: algebra and basic statistics
• No prior programming experience required
• No prior AI or machine learning knowledge required

Format and Access

• Duration: Approximately 12 hours of content
• Format: Self-paced online with video instruction, demonstrations, and quizzes
• Course Access: 6 months of full access from enrollment
• Completion Window: 90 days to complete coursework and the final exam
• Assessment: 4 module quizzes (30% of grade) + comprehensive final exam (70% of grade)
• Passing Score: 70% overall
• Language: English
• AI Tools: Encouraged for learning and exercises; prohibited during quizzes and the final exam

Path to Certification

EE400 is the first course in GIEE's AI in Energy curriculum and contributes to two professional certifications:

• CAGP — Certified AI for Grid Professional: Complete the 9 Core courses (EE400 through EE408) plus the 3 Grid Add-On courses (EE410, EE411, EE412), then pass the CAGP certification exam.
• CAEP — Certified AI for Energy Professional: Complete the 9 Core courses (EE400 through EE408) plus the 3 Energy Add-On courses (EE420, EE421, EE422), then pass the CAEP certification exam.

About the GIEE AI in Energy Curriculum

EE400 is part of a 15-course curriculum built specifically for energy and grid professionals. The curriculum follows a Shared Core architecture: 9 foundational courses every student takes, plus 6 specialized Add-On courses across two specialization tracks (Grid or Energy).

GIEE's pedagogical signature is "Grid-First with Energy Context": every concept is introduced through grid and utility applications, with meaningful coverage of renewable energy and broader energy industry use cases. This approach reflects authentic domain expertise and creates a defensible competitive position against generic AI education.