Programs & Courses
Engineering the Modern Grid. Powering the Future.
GIEE delivers professional education at the intersection of energy, intelligence, and infrastructure. Build expertise across Solar PV, Battery Energy Storage, and the most comprehensive Applied AI curriculum for the energy sector.
Renewable Energy
Foundational and applied courses in solar photovoltaic systems. From principles and components through advanced engineering design and grid integration.
EE200: Introduction to Solar Power Systems
Foundational course covering principles, components, and applications of solar photovoltaic systems.
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- Solar resource fundamentals and irradiance principles
- PV cell, module, and array technology overview
- Grid-tied and off-grid system architectures
- Component selection: panels, inverters, mounting, BOS
- Site assessment and basic system design workflow
- Solar physics and PV technology fundamentals
- System types: residential, commercial, utility-scale
- NEC code basics and safety essentials
- Performance, efficiency, and energy yield concepts
EE201: Advanced Solar Power Systems
Engineering-level course on the detailed design and sizing of solar PV systems. Master string design, inverter selection, and the workflow that produces specified systems.
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- Detailed PV system sizing methodology
- String design with voltage at temperature extremes
- Inverter selection and DC/AC ratio optimization
- Shading analysis and tilt/azimuth optimization
- Single-line diagram development and project documentation
- Sizing methodology across project scales
- String, central, and microinverter architectures
- MPPT input matching and parallel string considerations
- Worked design examples for residential, commercial, and utility-scale
EE202: Solar PV Electrical Design, Codes, and Operations
The electrical design, comprehensive code, interconnection, and operations course. Master NEC compliance, utility interconnection, solar plus storage integration, and operations and maintenance.
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- Detailed electrical design including wire sizing and overcurrent protection
- Grounding, bonding, and arc-fault protection per NEC requirements
- Comprehensive code mastery: NEC 690, 705, 706 and IFC fire code
- Utility interconnection process and IEEE 1547 application
- Commissioning, IV curve testing, and ongoing performance monitoring
- Wire sizing, voltage drop, and conduit fill calculations
- Net metering structures and permit drawing requirements
- Solar plus storage system design with battery integration
- Performance ratio analysis and warranty management
EE203: Solar PV Modeling, Simulation, and Energy Yield Analysis
Master the industry-standard tools and methods that produce bankable energy yield estimates. Build proficiency in PVsyst, SAM, and the modeling workflow that validates solar PV designs.
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- Apply solar resource data correctly: TMY, P50/P90, satellite vs ground
- Build PVsyst projects from climate data through detailed simulation results
- Use NREL’s System Advisor Model (SAM) as a powerful free alternative
- Perform P50/P90 analysis for project finance and risk assessment
- Validate production estimates against measured data using performance ratios
- Loss diagram interpretation and optimization opportunities
- Soiling, degradation, bifacial, and tracking modeling
- Tool selection framework: PVsyst vs SAM vs other platforms
- Common modeling errors and how to avoid them
Energy Storage
Comprehensive Battery Energy Storage System curriculum. From fundamentals through design, sizing, codes, and grid integration. Built for engineers deploying BESS at every scale.
EE220: BESS Fundamentals
Foundational understanding of Battery Energy Storage Systems for grid applications. Architecture, chemistries, components, and applications.
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- BESS architecture, components, and operating principles
- Battery chemistries: Li-ion, LFP, NMC, flow, and emerging
- Common BESS applications across grid scales
- Safety considerations, codes, and standards overview
- Economic and operational drivers for storage deployment
- Battery cell, module, and pack fundamentals
- Power conversion systems and EMS / BMS overview
- Use cases: peak shaving, frequency regulation, renewable firming
- BESS lifecycle, degradation, and asset management
EE221: BESS Design and Sizing
Practical, application-driven course on sizing and designing battery energy storage systems for real projects.
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- Load profile analysis and storage requirement derivation
- Power vs. energy sizing methodologies
- Component selection: battery, PCS, transformer, BOS
- Round-trip efficiency, depth of discharge, cycle life trade-offs
- Single-line diagram development for BESS projects
- Sizing for peak shaving, time-shift, and ancillary services
- Augmentation strategies and end-of-life planning
- Site layout, thermal management, and HVAC sizing
- Worked design examples across project scales
EE222: BESS Grid Integration: Codes and Operations
The grid-facing side of BESS. Interconnection, codes, protection schemes, and operational practices.
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- Interconnection requirements and study processes
- Key standards: IEEE 1547, UL 9540, NFPA 855, NEC Article 706
- Protection coordination and grid-forming vs. grid-following control
- BESS commissioning, testing, and acceptance criteria
- Operational practices and revenue stacking strategies
- Utility interconnection process end-to-end
- Inverter-based resource (IBR) characteristics
- Protection, fault response, and ride-through
- Market participation and dispatch optimization
AI in Energy
The most comprehensive applied-AI curriculum built specifically for energy and grid professionals. Built on a Shared Core architecture: 9 foundational courses every student takes, plus specialized Add-On tracks for grid or broader energy specialization.
Core Courses (9)
The shared foundation taken by all AI in Energy certification students. Required for both CAGP and CAEP certifications.
EE400: AI and ML Fundamentals for Energy
The foundational course of the AI in Energy curriculum. AI, ML, data, and core algorithms taught entirely through energy and grid examples.
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- Explain what AI is, where it sits in the broader landscape, and why it matters for energy
- Distinguish supervised, unsupervised, and reinforcement learning with energy use cases
- Apply core ML algorithms (regression, decision trees, random forests, XGBoost) to grid data
- Evaluate model quality using appropriate metrics (MAE, RMSE, MAPE, precision, recall)
- Engineer features from raw energy data (lag features, temporal features, weather)
- AI history, types (ANI/AGI/ASI), and the AI/ML/DL/GenAI hierarchy
- Data quality, types, and preprocessing for energy applications
- The complete machine learning workflow from problem to deployment
- Model evaluation, cross-validation, overfitting, and the bias-variance tradeoff
EE401: Deep Learning, LLMs and Generative AI
From neural networks through large language models. The mechanics behind modern AI, with energy-grounded examples throughout.
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- Build mental models of how neural networks learn through backpropagation
- Match neural network architectures to data: CNN for images, LSTM for time series, transformers for text
- Explain how large language models work and where their capabilities come from
- Use transfer learning and fine-tuning effectively for energy domain adaptations
- Apply generative AI methods (GANs, VAEs, diffusion models) to energy problems
- Neural network fundamentals, activation functions, depth versus width
- CNN, RNN, LSTM, autoencoder, and transformer architectures
- How attention mechanisms enable modern LLMs
- Fine-tuning versus RAG decision matrix
EE402: Explainable AI and MLOps
Make AI decisions transparent and operate AI models reliably in production. Critical for regulatory and safety-critical applications.
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- Apply SHAP and LIME for per-prediction and global model explanation
- Diagnose why a model made a specific prediction in regulatory contexts
- Implement complete MLOps pipelines: versioning, tracking, registry, CI/CD
- Detect and respond to data and concept drift in production models
- Design retraining pipelines appropriate to different model types
- Why “the AI said so” is unacceptable in energy and how to do better
- SHAP values, LIME, and feature importance methods
- MLOps lifecycle: data versioning, experiment tracking, model registry
- Production monitoring: tracking metrics, alerting, retraining triggers
EE403: Prompt Engineering and RAG
The most immediately practical course in the curriculum. Master prompt engineering and build a working RAG system grounded in energy documents.
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- Apply the RCFT framework (Role, Context, Format, Task) to construct effective prompts
- Use advanced techniques: chain-of-thought, few-shot, tree-of-thought reasoning
- Design and build complete RAG systems from document ingestion through generation
- Choose between prompting, fine-tuning, and RAG based on use case
- Build a production-ready RAG system with citations, monitoring, and evaluation
- Prompt engineering principles, frameworks, and energy-specific patterns
- RAG architecture, document chunking strategies, embedding models
- Vector databases (ChromaDB, Pinecone, Weaviate) and hybrid search
- Building, testing, and deploying RAG systems for utility documents
EE404: Agentic AI Systems for Energy
The frontier course of the Core. Design AI systems that plan, take action, and learn from outcomes. Multi-agent architectures for energy operations.
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- Distinguish traditional AI from agentic AI and identify when each fits
- Design agent architectures with appropriate memory, planning, and tool use
- Build multi-agent systems that coordinate across energy operations functions
- Implement human-in-the-loop safety patterns appropriate to energy contexts
- Evaluate when agentic AI is the right architecture versus simpler alternatives
- Agent architecture: LLM, goals, tools, memory, planning, safety
- Multi-agent systems for forecasting, monitoring, maintenance, markets
- Frameworks: LangChain, CrewAI, and the agent ecosystem
- Safety patterns and human-in-the-loop principles for energy applications
EE405: AI Across the Energy Value Chain
Strategic survey of AI applications from generation through markets. Provides the context that makes Add-On specialization meaningful.
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- Map AI applications across the full energy value chain
- Identify high-impact AI use cases at generation, transmission, distribution, customer levels
- Quantify the financial impact of AI deployments using industry benchmarks
- Recognize when AI is the right tool versus traditional engineering approaches
- Communicate AI value propositions to non-technical stakeholders
- AI value chain map: generation, transmission, distribution, retail, markets
- Load forecasting, renewable forecasting, predictive maintenance overviews
- AI at the customer interface: dynamic pricing, demand response
- Industry benchmarks and ROI frameworks for energy AI
EE406: Python AI Ecosystem and Cloud
The practical toolkit course. Python AI ecosystem, cloud AI platforms, and enterprise platform decisions for energy organizations.
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- Navigate the Python AI and data science ecosystem with confidence
- Choose appropriate libraries for data, ML, deep learning, LLM, MLOps tasks
- Compare cloud AI platforms (Azure ML, AWS SageMaker, Google Vertex AI)
- Build a personal learning path through Python AI tooling
- Identify the right platform decision based on existing enterprise commitments
- Data and ML: pandas, NumPy, scikit-learn, XGBoost, PyTorch
- LLM and RAG: LangChain, OpenAI SDK, Anthropic SDK
- MLOps: MLflow, FastAPI, Docker, Evidently AI
- Cloud platforms: Azure ML, AWS SageMaker, Google Vertex AI, edge AI
EE407: AI Strategy, Governance and Ethics
Senior-level coverage of AI business cases, organizational maturity, governance, bias, and responsible deployment in energy contexts.
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- Build credible AI business cases with ROI frameworks tailored to energy
- Assess and improve organizational AI maturity using a structured model
- Implement model risk management, governance, and human oversight policies
- Recognize and mitigate bias and fairness issues in energy AI applications
- Apply privacy-preserving techniques like federated learning where appropriate
- AI business case construction: revenue, cost, risk, strategic capability
- AI maturity model and governance framework for energy organizations
- Bias and fairness in energy AI applications
- Privacy-preserving AI: federated learning, differential privacy
EE408: Hands-On AI Capstone
The applied capstone of the Core. Three substantial hands-on exercises integrating prompt engineering, ML forecasting, and a complete RAG system.
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- Apply prompt engineering frameworks to a real outage scenario with multi-audience deliverables
- Build and evaluate a complete load forecasting workflow in Python
- Construct a working RAG system grounded in a real NERC standard
- Compare AI approaches against traditional engineering baselines using appropriate metrics
- Document AI work to standards appropriate for engineering peer review
- Capstone exercise 1: prompt engineering for major outage scenario
- Capstone exercise 2: load forecasting in Python with linear baseline and LSTM
- Capstone exercise 3: RAG system build for NERC TPL-001 in Google Colab
- Documentation and presentation standards for engineering AI work
Grid Add-On (3) — for CAGP
Specialized courses for engineers pursuing the CAGP certification. Deep coverage of AI in grid operations, DER integration, and utility-specific applications.
EE410: AI for Grid Operations and Smart Grid
Deep-dive on AI applications across grid operations: smart grid architecture, real-time monitoring, distribution automation, and modern utility control centers.
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- Architect AI-enhanced smart grid systems across all layers
- Apply AI to real-time grid monitoring including PMU data and SCADA streams
- Design automated distribution systems with AI-driven switching and voltage regulation
- Quantify AI impact on operational metrics: SAIDI, SAIFI, MAIFI, distribution losses
- Integrate AI coordination layers across AMI, SCADA, weather, and market data
- AI-powered smart grid architecture across all layers
- Real-time monitoring: PMU stability, contingency analysis, anomaly detection
- Distribution automation: smart switches, capacitor banks, voltage regulators
- AI for outage management: prediction, response, and restoration
EE411: AI for DER Integration and System Planning
The flagship CAGP course. AI for DER integration, hosting capacity, virtual power plants, and long-term system planning.
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- Apply AI methods across the full DER integration lifecycle
- Build hosting capacity analyses using AI-augmented techniques
- Architect virtual power plants coordinating thousands of distributed resources
- Develop AI-driven integrated resource planning workflows
- Communicate AI-augmented planning recommendations to regulators and executives
- DER integration challenges and AI-driven solutions
- Hosting capacity, voltage analysis, and impact studies augmented with AI
- Virtual power plant architectures and coordination algorithms
- AI-driven Integrated Resource Planning and capacity expansion
EE412: NLP, CV, and Predictive Maintenance
AI for utility-specific use cases: regulatory documents, drone and satellite inspection, transformer health, and predictive maintenance economics.
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- Apply natural language processing to utility-specific documents and workflows
- Design computer vision systems for transmission, distribution, substation inspection
- Build predictive maintenance models for transformers, lines, substation equipment
- Quantify the financial value of moving from preventive to predictive maintenance
- Architect end-to-end inspection and maintenance workflows with AI
- NLP for utilities: outage reports, work orders, regulatory analysis
- Computer vision: drone inspection, solar defect detection, vegetation management
- Substation visual monitoring with edge AI
- Predictive maintenance for transformers using DGA, thermal, loading data
Energy Add-On (3) — for CAEP
Specialized courses for engineers pursuing the CAEP certification. AI for energy markets, renewables, electric vehicles, and broader energy industry applications.
EE420: AI for Energy Markets and Trading
AI for wholesale markets, locational marginal pricing, ancillary services, battery dispatch, and demand response economics.
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- Build price forecasting models for energy and ancillary service markets
- Architect bidding strategies that combine generation, price, and demand forecasts
- Apply reinforcement learning to battery storage dispatch and arbitrage
- Quantify the financial impact of forecast accuracy improvements
- Design demand response programs using ML-driven flexibility prediction
- Wholesale market structure: energy, capacity, ancillary services
- Price forecasting methods, MAPE benchmarks, financial value
- Battery storage dispatch optimization with reinforcement learning
- Demand response program design and customer flexibility prediction
EE421: AI for Renewable Energy and Storage
AI for solar and wind forecasting, plant optimization, BESS operation, and high-renewable-penetration grid integration.
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- Build solar and wind forecasting models using satellite, NWP, and ML techniques
- Architect hybrid physics-ML models that combine domain knowledge with data
- Optimize battery energy storage operation across multiple revenue streams
- Apply AI to wind plant wake effects, turbine optimization, degradation prediction
- Quantify renewable AI value: curtailment, reserves, market participation
- Solar forecasting with satellite imagery, NWP, and CNN architectures
- Wind forecasting with non-linear speed-power relationships and wake modeling
- Hybrid physics-based + ML residual modeling: state of the art
- BESS operational optimization: dispatch, augmentation, end-of-life
EE422: AI for EV, Digital Twins and Cybersecurity
Three frontier applications: EV grid integration, digital twins for energy assets, and AI-driven cybersecurity for critical infrastructure.
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- Forecast EV charging load and design smart charging strategies (V1G and V2G)
- Architect digital twins for transformers, feeders, and renewable plants
- Apply AI to grid cybersecurity for OT networks and critical infrastructure
- Quantify the spatial and temporal grid impact of EV adoption
- Design fleet-to-grid programs that monetize EV battery flexibility
- EV load forecasting using telematics and charging history
- Smart charging V1G and Vehicle-to-Grid V2G strategies
- Digital twin pillars: physical asset, digital model, AI intelligence layer
- AI for grid cybersecurity: AI-SIEM, behavioral baseline, NERC CIP compliance
Mastery Bundles
Bundle related courses into a focused learning path at a discounted package price compared to individual courses. Mastery Bundles are the bridge between individual courses and full certification.
Solar PV Mastery
Comprehensive solar PV engineering capability.
The complete solar PV engineering foundation. Covering fundamentals, design and sizing, electrical and code compliance, and energy modeling and simulation.
Includes: EE200 Introduction + EE201 Advanced + EE202 Electrical and Codes + EE203 Modeling and Simulation
Coming SoonBESS Mastery
End-to-end battery energy storage expertise.
The complete BESS skillset. From fundamentals through design, sizing, codes, grid integration, and operations.
Includes: EE220 BESS Fundamentals + EE221 BESS Design and Sizing + EE222 BESS Grid Integration
Coming SoonAI in Energy Mastery
Where artificial intelligence meets the grid and energy.
The most comprehensive applied-AI curriculum for energy professionals. Includes the 9 Core courses plus all 6 Add-On courses across both Grid and Energy specializations.
Includes all 9 Core + 3 Grid Add-On + 3 Energy Add-On courses (EE400-EE422)
Coming SoonCertifications
Earn an industry-recognized certification by completing the corresponding course track and passing the certification examination. All certifications validate applied capability through rigorous coursework and exams.
Certified Solar PV Professional
Comprehensive solar PV credential
Validates applied solar PV engineering capability across foundations, design and sizing, electrical and code compliance, and energy modeling.
Certified BESS Professional
Battery energy storage credential
End-to-end BESS expertise from fundamentals through advanced design, codes, and grid integration.
Certified AI for Grid Professional
Applied AI for grid specialists
Validates mastery of AI methods across grid forecasting, DER integration, predictive maintenance, and agentic systems.
Certified AI for Energy Professional
Applied AI for energy professionals
For renewable developers, market analysts, energy strategists, and broader energy professionals applying AI across the value chain.
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