Master’s in Data Analytics Engineering for Grid Operation
Online master's programme of 1,900 hours in data analytics engineering for grid operation: 12 modules covering grid operational data, data quality, visualisation, load and distributed generation forecasting, state estimation, fault and asset analytics, data-driven optimisation, real-time analytics and secure deployment, with an assigned technical tutor.
Master’s in Data Analytics Engineering for Grid Operation — studying from Ireland
Ireland's energy sector is undergoing significant change, with a growing share of wind power, expanding grid infrastructure and investment in interconnection and storage. The Electricity Supply Board (ESB) and Gas Networks Ireland manage major transmission and distribution assets, while EirGrid operates the national electricity transmission system. Engineers work on grid planning, renewable integration, network operation and energy system modelling. Regulation is provided by the Commission for Regulation of Utilities, with national targets set by government climate and energy policy.
Fees for Ireland: €7,920 (Euro), with instalments and employer sponsorship available.
Relevant bodies in Ireland
Typical roles
Questions from Ireland
Which Irish bodies oversee energy and network engineering recognition?
The Commission for Regulation of Utilities regulates electricity and gas markets in Ireland, and EirGrid and ESB Networks operate the transmission and distribution systems. Engineers Ireland sets professional engineering standards and chartership. SEIUM's qualifications are private and not accredited by any national body, so their recognition is for employers or the relevant body to determine.
Do Irish energy and utility employers consider private online engineering qualifications?
Irish utilities, grid operators and consultancies generally prioritise professional registration, relevant degrees and operational experience. A private online qualification may support your application as evidence of study, but how it is weighed is at each employer's discretion. We cannot guarantee any outcome, so ask prospective employers directly.
Can I study alongside a job, and does my employer need to fund it?
The programme is online and taught in English, so you can study from home in Ireland with no travel and no student visa required. Plan for 1,900 hours on a master's, 800 on a postgraduate diploma or 300 on a specialist course. Each student has an assigned technical tutor, fees can be paid in instalments, and employer funding is possible but not required, so it is worth raising with your employer.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Prepare operational grid data with quality assurance.
Design dashboards for grid operation.
Develop forecasting models for load and distributed generation.
Improve observability with data-driven state estimation.
Analyse events, faults and asset health.
Apply real-time analytics to operation.
Deploy analytical models securely in OT environments.
Who it's for
- Grid data analysts aiming to take on more advanced modelling.
- Data scientists working on network operation.
- Asset analytics engineers.
- Energy forecasting specialists.
- Control centre engineers.
- Developers of analytics solutions for utilities.
- Engineering or science graduates aiming to move into electricity transmission and distribution.
What you'll work with
- IEC 61970 (CIM for energy management systems)
- IEC 61968 (CIM for distribution)
- IEEE C37.118.1 (synchrophasor measurements)
- IEC 60599 (interpretation of dissolved gas analysis)
- IEC 60076-7 (thermal ageing of power transformers)
- Regulation (EU) 2016/679 (GDPR)
- IEC 62443 (industrial automation and control systems security)
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Python (pandas, scikit-learn, PyTorch)
- Grafana
- Apache Kafka
- InfluxDB
- pandapower
12 modules · 1900 h
1.1Data sources: SCADA, PMU, AMI and protection devices1.2Volume, velocity and variety1.3Time resolution of data1.4Asset and GIS data1.5Observed weather data1.6Event and outage data1.7The operator's data architecture1.8Analytical use cases1.9Value of analytics for the grid1.10Case study: data inventory of a control centre
2.1Time-series cleansing2.2Imputation of missing data2.3Outlier detection2.4Time alignment of signals2.5Resampling and aggregation2.6Validation with physical rules2.7Metadata management2.8Quality of topology data2.9Automating data quality2.10Lab: cleansing SCADA data with pandas
3.1Operational dashboards3.2Geospatial visualisation of the grid3.3Load heat maps3.4Duration curves3.5Peak and trend analysis3.6Quality-of-service indicators3.7Event visualisation3.8Visualisation tools3.9Design for operators3.10Practical exercise: transformer loading dashboard in Grafana
4.1Drivers of electricity demand4.2Very short-term forecasting4.3Day-ahead and week-ahead forecasting4.4Seasonal SARIMA models4.5Gradient boosting models4.6LSTM neural networks4.7Hierarchical forecasting by level4.8Probabilistic forecasting with quantiles4.9Evaluation and monitoring4.10Lab: substation load forecasting with LSTM
5.1Visibility of behind-the-meter generation5.2Estimation of unmonitored photovoltaics5.3Solar forecasting with satellite imagery5.4Short-term wind forecasting5.5Nowcasting models5.6Net load forecasting5.7Uncertainty and ramps5.8Aggregation of forecasts5.9Use in operation5.10Practical exercise: behind-the-meter photovoltaic disaggregation
6.1Classical state estimation6.2State estimation with PMUs6.3Data-driven pseudo-measurements6.4Machine learning-based estimation in distribution6.5Bad data detection6.6Grid observability6.7Optimal sensor placement6.8Topology identification6.9Validation of results6.10Lab: state estimation with learnt pseudo-measurements
7.1Fault classification7.2Data-driven fault location7.3Analysis of oscillographic records7.4Incipient fault detection7.5Root-cause analysis of outages7.6Correlation with weather7.7Vegetation analytics7.8Storm outage prediction7.9Improving quality-of-supply indices7.10Case study: wind-driven outage risk model
8.1Asset health indices8.2Dissolved gas analysis8.3Thermal ageing of transformers8.4Survival models8.5Cable failure prediction8.6Maintenance prioritisation8.7Replacement optimisation8.8Drone and image-based inspection8.9Integration with asset management8.10Practical exercise: survival model for a cable fleet
9.1Network reconfiguration9.2Data-driven volt-var control9.3Congestion management9.4Maintenance scheduling9.5Flexibility dispatch9.6Optimisation under uncertainty9.7Reinforcement learning in grids9.8Decision support systems9.9Benefit assessment9.10Lab: volt-var control with reinforcement learning
10.1Data streaming10.2Complex event processing10.3Lambda and kappa architectures10.4Edge analytics10.5Smart alarms and prioritisation10.6Latency and availability10.7Integration with SCADA10.8Streaming tools10.9Platform scalability10.10Practical exercise: streaming anomaly detection with Kafka
11.1Governance of operational data11.2Deploying models in OT11.3Validation with operators11.4Explainability for the control room11.5Cybersecurity of analytics platforms11.6Data access management11.7Customer data privacy11.8Model life-cycle management11.9Measuring value11.10Workshop: deploying a model in the control centre
12.1Definition of the operational problem12.2Data acquisition and preparation12.3Exploratory data analysis12.4Model development12.5Validation with real data12.6Integration into operation12.7Benefit assessment12.8Conclusions and future lines of work12.9Master's Thesis report12.10Master's Thesis: defence before a panel
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 (pandas, scikit-learn, PyTorch), Grafana, Apache Kafka, InfluxDB and pandapower, in free, academic or evaluation versions. Practical work is based on real procedures: cleansing and analysis of SCADA data, substation load forecasting, and asset and outage risk models. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: IEC 61970, IEC 61968, IEEE C37.118.1, IEC 60599, IEC 60076-7, Regulation (EU) 2016/679, IEC 62443, Regulation (EU) 2024/1689.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
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.
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 (pandas, scikit-learn, PyTorch), Grafana, Apache Kafka, InfluxDB and pandapower, 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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