Centram Centre for Applied Artificial Intelligence

AI Automation Course for Electrify Regulation

Target audience:

Regulators, policy analysts, utility analysts, compliance officers, data scientists working in energy, and technical staff supporting regulatory functions.

Course duration:

This course is delivered in 2 weeks physical bootcamp training plus 3 weeks of online study OR 4 weeks of physical boot camp whichever is convenient to participants

Course description

This course empowers participants how AI/ML and automation tools can be applied safely and effectively to electricity regulation. It combines regulatory economics and sector knowledge with practical machine learning, time-series analysis, automation (RPA and workflow orchestration), MLOps, explainability, data governance, and ethics. Students build end-to-end pipelines for common regulatory tasks: market monitoring, tariff analytics, reliability assessment, compliance reporting, consumption forecasting, and anomaly detection.

Learning outcomes

By the end of the course students will be able to:

  1. Translate regulatory questions into data/analytics requirements
  2. Build and evaluate ML models for load/price forecasting, anomaly detection, and classification
  3. Design automated workflows for data ingestion, monitoring, and reporting
  4. Apply explainability, model validation and governance to AI systems in a regulatory context
  5. Implement basic MLOps and deployment strategies for production-grade analytics
  6. Identify legal, ethical and cybersecurity issues and mitigation strategies

Course content

Introduction & Use Cases

  1. Topics: Role of analytics in modern regulation; key regulatory tasks aided by AI (market monitoring, tariff design, compliance, forecasting, demand response, theft detection); success stories and failure modes
  2. Lab: Case study mapping regulatory questions to data & analytics solutions
  3. Deliverable: Short proposal outlining a capstone project idea

Data Foundations & Energy Datasets

  1.  Topics: Types of energy data (SCADA, AMI/smart meters, market bids/trades, outage logs, weather, GIS), data quality, missing data, time alignment, privacy and anonymization, data provenance
  2. Tools: SQL, Pandas, time-series pre-processing techniques
  3. Lab: Ingest and clean sample AMI and market data; build an ETL pipeline
  4. Readings/Datasets: EIA, ENTSO-E transparency platform, OpenEI, local market operator data, IEEE benchmark datasets

Time-Series Analysis & Forecasting

  1. Topics: Exploratory analysis of load and price series, stationarity, ARIMA/SARIMA, exponential smoothing, state-space models, seasonal decomposition, evaluation metrics (MAE, RMSE, MAPE), cross-validation for time series
  2. Lab: Baseline load and price forecasting models on historical data
  3.  Deliverable: Forecast baseline report

Machine Learning for Energy Applications

  1. Topics: Supervised learning (regression/classification), tree-based models (Random Forest, XGBoost), feature engineering for temporal/spatial data, categorical encoding, hyperparameter tuning
  2. Lab: Develop ML models for short-term load/price forecasting and feature importance analysis

Anomaly Detection & Market Monitoring

  1. Topics: Unsupervised and semi-supervised methods (clustering, isolation forest, autoencoders), statistical process control, detecting market abuse (price manipulation, collusion), event detection from streaming data
  2. Lab: Implement anomaly detection for fraud/theft and abnormal bidding behaviour; visualization dashboard with alerting
  3. Deliverable: Anomaly detection demo and investigation workflow

Optimization & Simulation for Regulatory Design

  1. Topics: Optimization for inspection scheduling, outage restoration prioritization, tariff and subsidy simulation, market simulation basics (agent-based models), scenario analysis for policy decisions
  2. Lab: Small optimization problem (e.g., inspection schedule) and scenario modeling for tariff changes

Automation & Orchestration

  1. Topics: Workflow orchestration (Airflow, Prefect), RPA for regulatory reporting (UiPath/Automation Anywhere ideas), API automation, real-time vs batch processing, alerting and operationalization
  2. Lab: Build an automated ETL + model scoring pipeline with scheduled runs and alerting

 MLOps, CI/CD, and Model Governance

  1. Topics: Model versioning (MLflow), testing and monitoring models in production, drift detection, rollback strategies, data and model lineage, auditability for regulators
  2. Lab: Package a model with versioning, set up simple monitoring and drift detection

 Explainability, Fairness & Legal Considerations

  1. Topics: Explainable ML (SHAP, LIME), reporting model decisions to stakeholders, fairness in tariffs and enforcement, data privacy (GDPR-style), recordkeeping and audit trails, regulatory acceptance criteria
  2. Lab: Generate explainability reports and produce a compliance-ready model documentation (model factsheet)

 Cybersecurity, Risk Management & Ethics

  1. Topics: Attack surfaces for ML systems, adversarial examples, secure model deployment, business continuity, risk assessment frameworks, ethical considerations of automated enforcemen
  2. Activity: Table top exercise simulating a model failure/cyber incident and regulator response plan

Capstone Project Presentations & Course Wrap-up

  1. Participants present capstone projects: end-to-end analytics or automation solution for a regulatory problem (e.g., market surveillance dashboard, tariff impact simulator, theft detection pipeline, automated compliance reporting

Capstone project ideas:

  1. Automated market surveillance system detecting abnormal bids, with explainable alerts
  2.  Short-term load & price forecasting combined with tariff impact simulation for tariff review
  3. Anomaly detection for AMI data to detect theft/leakage and prioritize inspections
  4. Compliance automation: automated generation and submission-ready reports using RPA
  5. Demand response targeting model that segments customers and predicts baseline reduction with fairness constraints

Tools & technologies recommended

  1. Languages: Python (Pandas, scikit-learn, XGBoost, TensorFlow/PyTorch), R optional
  2. Time-series: statsmodels, Prophet, tsfresh
  3. Orchestration & MLOps: Airflow/Prefect, MLflow, Docker, Kubernetes (intro)
  4. Data stores: PostgreSQL, Hive/Parquet, cloud storage (S3, Azure Blob)
  5. BI/visualization: Power BI, Tableau, Dash/Streamlit
  6. RPA: UiPath (concepts), API automation using Python requests
  7. Cloud: AWS/GCP/Azure or Databricks for scalable compute
  8. Security/ops: logging, Prometheus/Grafana for monitoring

Readings & resources

  1. “Machine Learning for Energy Systems” (various papers)
  2. ENTSO-E / EIA / IEA reports and data portals
  3. Model Risk Management guidelines (e.g., FRB/SF for context) adapted to regulatory needs
  4. Papers on market surveillance and anomaly detection in electricity markets
  5.  SHAP/LIME explainability papers and tutorials

Ethics, governance and stakeholder engagement:

  1.  Emphasize transparency to regulated entities and public
  2. Model documentation, impact assessments, and public consultation practices
  3.  Mechanisms for human oversight and appeal in automated enforcement decisions

Deliverables for a course run:

  •  Slide deck per module
  •  Lab notebooks (Jupyter) with sample datasets
  • ETL and ML pipeline templates (Dockerized)
  •  Model factsheet and governance checklist templates
  •  Capstone evaluation rubric