Master'sEnergy and Networks Applications open

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.

Duration1900 h

ModeOnline

LanguageEnglish

Start datesSet by the faculty

AwardSEIUM Institute diploma

Enrol now View syllabus Ask an advisor

Master’s in Data Analytics Engineering for Grid Operation — studying from New Zealand

New Zealand's energy sector is distinctive for its high share of renewable generation, drawn from hydroelectric, geothermal and wind resources. Transpower operates the national grid, while distribution networks are run by regional lines companies, and the Electricity Authority regulates the wholesale and retail markets. Energy engineers are employed across generation, transmission, distribution and retail, with demand growing as the country works towards its renewable electricity goals.

Fees for New Zealand: NZ$15,700 (New Zealand dollar), with instalments and employer sponsorship available.

Relevant bodies in New Zealand

Electricity Authority
Transpower New Zealand
Energy Efficiency and Conservation Authority (EECA)

Typical roles

Power Systems Engineer
Network Planning Engineer
Grid Engineer
Energy Project Engineer

Questions from New Zealand

What professional recognition applies to energy and network engineers in New Zealand?

Engineering New Zealand administers Chartered Professional Engineer registration, which is widely used in the energy sector. The Electricity Authority oversees the electricity market, and the Electricity Engineers' Association is an industry body. SEIUM's qualifications are private and not accredited by any national body, so recognition is decided by employers or the relevant body.

Who employs energy and network engineers in New Zealand?

Major employers include electricity lines companies, generators, transmission operators, gas network businesses and consultancies. Roles often value registration, field experience and knowledge of New Zealand's grid and regulation. A private qualification may support your CV, but it does not guarantee employment or a particular career outcome.

Can I study from New Zealand with a full-time job, and does an employer need to fund it?

Yes. The programmes are 100% online and taught in English, so you can study from New Zealand without a student visa. You will have an assigned technical tutor and complete a final project. Specialist courses take 300 hours, diplomas 800 and master's programmes 1,900. Fees can be paid in instalments, and employer sponsorship is possible.

Virtual lab · try it before you enrol

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.

SIM · LIVE ●

Learning outcomes

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.

Profile

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.

Standards & tools

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

Syllabus

12 modules · 1900 h

1.1Data sources: SCADA, PMU, AMI and protection devices
1.2Volume, velocity and variety
1.3Time resolution of data
1.4Asset and GIS data
1.5Observed weather data
1.6Event and outage data
1.7The operator's data architecture
1.8Analytical use cases
1.9Value of analytics for the grid
1.10Case study: data inventory of a control centre

2.1Time-series cleansing
2.2Imputation of missing data
2.3Outlier detection
2.4Time alignment of signals
2.5Resampling and aggregation
2.6Validation with physical rules
2.7Metadata management
2.8Quality of topology data
2.9Automating data quality
2.10Lab: cleansing SCADA data with pandas

3.1Operational dashboards
3.2Geospatial visualisation of the grid
3.3Load heat maps
3.4Duration curves
3.5Peak and trend analysis
3.6Quality-of-service indicators
3.7Event visualisation
3.8Visualisation tools
3.9Design for operators
3.10Practical exercise: transformer loading dashboard in Grafana

4.1Drivers of electricity demand
4.2Very short-term forecasting
4.3Day-ahead and week-ahead forecasting
4.4Seasonal SARIMA models
4.5Gradient boosting models
4.6LSTM neural networks
4.7Hierarchical forecasting by level
4.8Probabilistic forecasting with quantiles
4.9Evaluation and monitoring
4.10Lab: substation load forecasting with LSTM

5.1Visibility of behind-the-meter generation
5.2Estimation of unmonitored photovoltaics
5.3Solar forecasting with satellite imagery
5.4Short-term wind forecasting
5.5Nowcasting models
5.6Net load forecasting
5.7Uncertainty and ramps
5.8Aggregation of forecasts
5.9Use in operation
5.10Practical exercise: behind-the-meter photovoltaic disaggregation

6.1Classical state estimation
6.2State estimation with PMUs
6.3Data-driven pseudo-measurements
6.4Machine learning-based estimation in distribution
6.5Bad data detection
6.6Grid observability
6.7Optimal sensor placement
6.8Topology identification
6.9Validation of results
6.10Lab: state estimation with learnt pseudo-measurements

7.1Fault classification
7.2Data-driven fault location
7.3Analysis of oscillographic records
7.4Incipient fault detection
7.5Root-cause analysis of outages
7.6Correlation with weather
7.7Vegetation analytics
7.8Storm outage prediction
7.9Improving quality-of-supply indices
7.10Case study: wind-driven outage risk model

8.1Asset health indices
8.2Dissolved gas analysis
8.3Thermal ageing of transformers
8.4Survival models
8.5Cable failure prediction
8.6Maintenance prioritisation
8.7Replacement optimisation
8.8Drone and image-based inspection
8.9Integration with asset management
8.10Practical exercise: survival model for a cable fleet

9.1Network reconfiguration
9.2Data-driven volt-var control
9.3Congestion management
9.4Maintenance scheduling
9.5Flexibility dispatch
9.6Optimisation under uncertainty
9.7Reinforcement learning in grids
9.8Decision support systems
9.9Benefit assessment
9.10Lab: volt-var control with reinforcement learning

10.1Data streaming
10.2Complex event processing
10.3Lambda and kappa architectures
10.4Edge analytics
10.5Smart alarms and prioritisation
10.6Latency and availability
10.7Integration with SCADA
10.8Streaming tools
10.9Platform scalability
10.10Practical exercise: streaming anomaly detection with Kafka

11.1Governance of operational data
11.2Deploying models in OT
11.3Validation with operators
11.4Explainability for the control room
11.5Cybersecurity of analytics platforms
11.6Data access management
11.7Customer data privacy
11.8Model life-cycle management
11.9Measuring value
11.10Workshop: deploying a model in the control centre

12.1Definition of the operational problem
12.2Data acquisition and preparation
12.3Exploratory data analysis
12.4Model development
12.5Validation with real data
12.6Integration into operation
12.7Benefit assessment
12.8Conclusions and future lines of work
12.9Master's Thesis report
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 (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.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.

Standards-based learning:guided reading of IEC 61970, IEC 61968 and IEEE C37.118.1 applied to real cases, not as a theoretical annex.

Industry software:Python (pandas, scikit-learn, PyTorch), Grafana, Apache Kafka, InfluxDB and pandapower, in free, academic or evaluation versions.

Master's Thesis with defence:A complete analytics solution for a real grid operation problem, 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.

Operational problem and data

Define a grid operation problem and prepare SCADA, PMU or AMI data with quality checks.

Exploratory analysis and dashboard

Explore the data and build an operational dashboard in Grafana.

Forecasting or estimation model

Develop a load, generation or state estimation model and validate it with real data.

Real-time integration

Integrate the model into operation with streaming analytics and secure OT deployment.

Master's Thesis

A complete grid analytics solution with benefit assessment, defended before a panel.

Career opportunities

Where this takes you

Grid data analystData scientist for grid operationAsset analytics engineerEnergy forecasting specialistControl centre engineerUtility analytics solutions developer

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 (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.

Keep exploring

Related programmes

See all in Energy and Networks

Master'sNZ$13,700

Master’s in Energy for Data Centres

Online master's programme of 1,900 hours in energy for data centres — UPS/STS, cooling, PUE and continuity. 12 modules covering data centres and energy consumption, power…

12–14 monthsOnlineEnergy and Networks

Master'sNZ$13,700

Master’s in Probabilistic Analysis and Network Resilience

Online master's programme of 1,900 hours in probabilistic analysis and power network resilience, covering contingencies, extreme weather and resilience planning. 12 modules covering reliability, Monte Carlo…

1,900 hours · 12–14 monthsOnlineEnergy and Networks

Master'sNZ$13,700

Master’s in Power-to-X Engineering

Online master's programme of 1,900 hours in Power-to-X engineering, structured in 12 modules covering carbon and nitrogen sources, catalytic reactors, green ammonia, renewable methanol, synthetic fuels…

1900 hOnlineEnergy and Networks

Master’s in Data Analytics Engineering for Grid OperationEnrol

Related programmes

More in Energy and Networks

Master’s in Renewables & Distributed Generation EngineeringView programme →Master’s in DC and Nano Networks EngineeringView programme →Master’s in Data Centre Energy EngineeringView programme →Master’s in Onshore/Offshore Wind: Engineering and O&M: LayoutView programme →Master’s in Digital Substations (IEC 61850)View programme →Master’s in Energy Risk Management and Hedging EngineeringView programme →

All Energy and Networks programmes → · How to apply · Fees & financing · Scholarships