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 Singapore

Singapore's energy system is largely based on natural gas, supplemented by growing investment in solar, energy storage and grid modernisation. The Energy Market Authority regulates the electricity and gas markets and oversees network reliability, while SP Group operates the national grid. Engineers work on power systems, distribution networks, energy efficiency and resilience planning. The sector also supports regional energy trading and the petrochemical cluster on Jurong Island.

Fees for Singapore: S$11,300 (Singapore dollar), with instalments and employer sponsorship available.

Relevant bodies in Singapore

Energy Market Authority (EMA)
SP Group
Singapore Power Engineering Association
Institution of Engineers Singapore (IES)

Typical roles

Power Systems Engineer
Grid Engineer
Energy Efficiency Engineer
Network Planning Engineer

Questions from Singapore

Which Singapore bodies regulate energy and utility networks, and is SEIUM recognised?

Singapore's energy sector is regulated by the Energy Market Authority (EMA), and power and water networks involve SP Group and PUB, the national water agency. Engineers may register with the Professional Engineers Board (PEB). SEIUM's qualifications are private, not state degrees, and not accredited by any national body, so recognition is for employers or the relevant body to decide.

Which employers in Singapore hire energy and network engineers?

Main employers include SP Group, PUB, EMA-regulated energy companies, engineering consultancies, and industrial firms managing power systems. Singapore's grid modernisation and renewable projects also create roles. Employers typically look for professional registration and practical network experience. Your final project may help, but it does not guarantee a position.

Is this workable alongside a job, and what support is provided?

The course is fully online and taught in English, with an assigned technical tutor for guidance. Workload is significant: 1,900 hours for a master's, 800 for a postgraduate diploma and 300 for a specialist course. Fees are payable in instalments, and employer sponsorship is possible, depending on your employer's training policy.

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'sS$11,300

Master’s in Port Electrification Engineering

Online master's programme of 1,900 hours in port electrification engineering: 12 modules covering onshore power supply (OPS), port machinery electrification, port electrical infrastructure, renewables and storage,…

1900 hOnlineEnergy and Networks

Master'sS$8,450

Master’s in LCA/LCC and Green Taxonomy: Life Cycle Assessment

Online master's programme of 1,900 hours in LCA/LCC and the green taxonomy, with 12 modules covering life cycle inventory and impact assessment, carbon footprint, environmental product…

1,900 hours · 12–14 monthsOnlineEnergy and Networks

Master'sS$8,450

Master’s in District Energy and Heating/Cooling Networks

Online master's programme of 1,900 hours in district energy and heating/cooling networks, 12 modules covering heat demand, generation sources, centralised cooling, hydraulic design, piping, substations, thermal…

1,900 hours · 12–14 monthsOnlineEnergy and Networks

Master’s in Data Analytics Engineering for Grid OperationEnrol

Related programmes

More in Energy and Networks

Master’s in Utility-Scale BESS EngineeringView programme →Master’s in Electrical Systems Engineering: Planning, Operation & MarketsView programme →Master’s in Power Electronics Engineering for Grids and BESS (complement to your core degree)View programme →Master’s in Verticals Engineering and Special ApplicationsView programme →Master’s in Smart Metering and AMI Data Quality EngineeringView programme →Master’s in Energy Retrofit and Certification (LEED/BREEAM)View programme →

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