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Master’s in Data, AI & Cyber Engineering in Energy

Online master's programme of 1,900 hours in data, AI and cybersecurity engineering for the energy sector: 12 modules covering energy data engineering, machine learning, forecasting, AI in operations, MLOps, digital twins, OT cybersecurity, digital regulation, market and customer analytics and data strategy, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Build reliable energy data pipelines.

02

Apply machine learning to energy-sector problems.

03

Develop forecasting models for demand, generation and prices.

04

Deploy models to production using MLOps practices.

05

Design digital twins of assets and networks.

06

Apply OT cybersecurity frameworks in energy.

07

Comply with European data and artificial intelligence regulation.

Profile

Who it's for

  • Energy data scientists looking to deepen their domain expertise.
  • Data engineers working for utilities.
  • AI specialists applying their skills to energy.
  • OT cybersecurity analysts in the power sector.
  • Digital twin architects.
  • Digital transformation leads in energy companies.
  • Engineering or science graduates aiming to move into electricity distribution.
Standards & tools

What you'll work with

  • Regulation (EU) 2024/1689 (Artificial Intelligence Act)
  • Regulation (EU) 2023/2854 (Data Act)
  • Regulation (EU) 2016/679 (GDPR)
  • Directive (EU) 2022/2555 (NIS2)
  • Delegated Regulation (EU) 2024/1366 (network code on cybersecurity)
  • IEC 62443 (industrial automation and control systems security)
  • IEC 62351 (power system communications security)
  • IEC 61970/61968 (CIM, Common Information Model)
  • Python (scikit-learn, PyTorch)
  • Apache Spark
  • MLflow
  • Grafana
  • InfluxDB
Syllabus

12 modules · 1900 h

  1. 1.1Digitalisation of the energy value chain
  2. 1.2Data in generation, networks and consumption
  3. 1.3EU action plan on digitalising the energy system
  4. 1.4Utility data architectures
  5. 1.5IT/OT convergence
  6. 1.6Data-driven business models
  7. 1.7Digital maturity of utilities
  8. 1.8Energy data governance
  9. 1.9Transformation cases
  10. 1.10Case study: digital maturity assessment of a utility
  1. 2.1Sources: SCADA, AMI, sensors and markets
  2. 2.2High-frequency time series
  3. 2.3Time-series databases
  4. 2.4Ingestion pipelines
  5. 2.5Data quality and cleansing
  6. 2.6CIM data models
  7. 2.7Energy data spaces
  8. 2.8Cloud platforms
  9. 2.9Data lakes and lakehouses
  10. 2.10Lab: SCADA data pipeline with Python
  1. 3.1Regression and classification
  2. 3.2Decision trees and gradient boosting
  3. 3.3Time-series cross-validation
  4. 3.4Feature selection
  5. 3.5Interpretability with SHAP
  6. 3.6Anomaly detection
  7. 3.7Clustering of profiles
  8. 3.8Error metrics
  9. 3.9Modelling best practice
  10. 3.10Practical exercise: classification of consumption profiles
  1. 4.1Demand forecasting
  2. 4.2Solar and wind forecasting
  3. 4.3Price forecasting
  4. 4.4Classical statistical models
  5. 4.5Recurrent neural networks and transformers
  6. 4.6Probabilistic forecasting with quantiles
  7. 4.7Numerical weather prediction data
  8. 4.8Forecast evaluation
  9. 4.9Operationalising models
  10. 4.10Lab: wind forecasting with LightGBM
  1. 5.1Predictive maintenance of assets
  2. 5.2Fault detection in plants
  3. 5.3AI-based dispatch optimisation
  4. 5.4Reinforcement learning
  5. 5.5Computer vision in inspections
  6. 5.6Generative AI in utilities
  7. 5.7Assistants for operators
  8. 5.8Model robustness and security
  9. 5.9Limitations of AI
  10. 5.10Case study: predictive maintenance of wind turbines
  1. 6.1Model life cycle
  2. 6.2Data and model versioning
  3. 6.3Deployment to production
  4. 6.4Drift monitoring
  5. 6.5Edge computing
  6. 6.6Integration with OT systems
  7. 6.7Automated retraining
  8. 6.8Model governance
  9. 6.9Open-source MLOps tools
  10. 6.10Practical exercise: deploying a forecasting model with MLflow
  1. 7.1The digital twin concept
  2. 7.2Asset and network twins
  3. 7.3Physical and hybrid models
  4. 7.4Synchronisation with real data
  5. 7.5Scenario simulation
  6. 7.6Digital twin platforms
  7. 7.7Cases in generation and networks
  8. 7.8Return on investment
  9. 7.9Scaling challenges
  10. 7.10Lab: hybrid digital twin of a transformer
  1. 8.1Threats to the energy sector
  2. 8.2Historic attacks on power grids
  3. 8.3OT security architecture
  4. 8.4IEC 62443 and IEC 62351
  5. 8.5The NIS2 Directive in energy
  6. 8.6Network code on cybersecurity
  7. 8.7OT intrusion detection
  8. 8.8Incident response
  9. 8.9Cybersecurity exercises
  10. 8.10Workshop: threat modelling of a renewable plant
  1. 9.1GDPR and consumption data
  2. 9.2The Artificial Intelligence Act
  3. 9.3The Data Act and device data
  4. 9.4Interoperability of metering data
  5. 9.5Data space governance
  6. 9.6AI ethics in energy
  7. 9.7Bias and fairness
  8. 9.8Algorithmic transparency towards customers
  9. 9.9Regulatory compliance of projects
  10. 9.10Practical exercise: risk assessment of a system under the AI Act
  1. 10.1Electricity market analytics
  2. 10.2Algorithmic energy trading
  3. 10.3Customer segmentation
  4. 10.4Churn prediction
  5. 10.5Personalised offers
  6. 10.6Energy poverty and data
  7. 10.7Electric vehicle charging analytics
  8. 10.8Flexibility platforms
  9. 10.9Visualisation and dashboards
  10. 10.10Lab: customer churn model for an energy retailer
  1. 11.1Use cases and prioritisation
  2. 11.2Return on data projects
  3. 11.3Data teams in utilities
  4. 11.4Change management
  5. 11.5Buy versus build
  6. 11.6Technology partnerships with suppliers
  7. 11.7Risk management
  8. 11.8Value indicators
  9. 11.9Digital roadmap
  10. 11.10Case study: AI roadmap for a distribution system operator
  1. 12.1Definition of the use case
  2. 12.2Data acquisition and preparation
  3. 12.3Model development
  4. 12.4Validation and explainability
  5. 12.5Deployment and monitoring
  6. 12.6Cybersecurity assessment
  7. 12.7Value assessment
  8. 12.8Conclusions and future lines of work
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There is no fixed timetable: you progress at your own pace within the intake, so it is compatible with full-time work.

Each module combines theory, guided reading of the original standards and applied exercises. The working software is Python (scikit-learn, PyTorch), Apache Spark, MLflow, Grafana and InfluxDB, in free, academic or evaluation versions. Practical work is based on real procedures: forecasting models with real data, anomaly detection in SCADA data and OT threat modelling exercises. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2024/1689, Regulation (EU) 2023/2854, Regulation (EU) 2016/679, Directive (EU) 2022/2555, Delegated Regulation (EU) 2024/1366, IEC 62443, IEC 62351, IEC 61970/61968.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the AI Act (Regulation (EU) 2024/1689), the Data Act (Regulation (EU) 2023/2854) and the GDPR applied to real cases, not as a theoretical annex.
Industry software:Python (scikit-learn, PyTorch), Apache Spark, MLflow, Grafana and InfluxDB, in free, academic or evaluation versions.
Master's Thesis with defence:A complete data and AI solution for an energy use case, from data to secure deployment, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Use case and data pipeline
Define an energy use case and build the data pipeline from SCADA or metering sources.
Forecasting or ML model
Develop and validate a model, such as a wind forecast with LightGBM, with SHAP explainability.
MLOps deployment
Deploy and monitor the model in production with MLflow.
Cybersecurity and compliance
Model OT threats and assess the system's risk under the AI Act.
Master's Thesis
A complete data and AI solution with value assessment, defended before a panel.
Career opportunities

Where this takes you

Energy data scientistUtility data engineerAI specialist for energyOT cybersecurity analystDigital twin architectDigital transformation lead
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python (scikit-learn, PyTorch), Apache Spark, MLflow, Grafana and InfluxDB, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme's software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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