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CourseAutomotive & Motorsport Applications open

Edge Computing in Energy Networks Course

Online 300 h course in edge computing for energy networks. 6 modules covering IEC 62443-4-2, IEC 61850 and Docker. Assigned tutor and open enrolment.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Understand edge computing architectures applied to energy.

02

Select hardware and software platforms for edge devices.

03

Deploy containerised applications and data flows at the edge.

04

Implement analytics and lightweight model inference on constrained devices.

05

Apply edge computing to microgrid, charging and substation control.

06

Secure distributed devices in accordance with the IEC 62443 series.

07

Design and implement an edge solution for an energy network.

Profile

Who it's for

  • Industrial IoT engineer who wants to specialise in edge computing in energy networks.
  • Edge systems architect who wants to specialise in edge computing in energy networks.
  • Network automation engineer who wants to specialise in edge computing in energy networks.
  • Embedded software developer who wants to specialise in edge computing in energy networks.
  • Digital substation specialist who wants to specialise in edge computing in energy networks.
  • Energy digital transformation consultant who wants to specialise in edge computing in energy networks.
  • Engineering or science graduates looking to enter electricity distribution.
Standards & tools

What you'll work with

  • IEC 62443-4-2
  • IEC 61850
  • IEC 61499
  • ISO/IEC 30141
  • IEC 62351
  • OPC UA (IEC 62541)
  • Regulation (EU) 2024/2847 on cyber resilience
  • IEEE 2030.5
Syllabus

6 modules · 300 h

  1. 1.1Cloud, fog and edge paradigms
  2. 1.2Latency requirements in energy control
  3. 1.3Edge devices in substations and plants
  4. 1.4IIoT reference architectures
  5. 1.5Industrial gateways and controllers
  6. 1.6Edge in meters and transformer centres
  7. 1.7Advantages over centralised processing
  8. 1.8Limitations of computing resources
  9. 1.9Use cases in energy networks
  10. 1.10Workshop: selecting edge hardware for a transformer centre
  1. 2.1Embedded operating systems and industrial Linux
  2. 2.2Containers with Docker on edge devices
  3. 2.3Lightweight orchestration with K3s
  4. 2.4Azure IoT Edge and AWS IoT Greengrass platforms
  5. 2.5Node-RED for data flows
  6. 2.6MQTT messaging and local brokers
  7. 2.7OPC UA at the edge
  8. 2.8Remote management of device fleets
  9. 2.9Secure OTA updates
  10. 2.10Laboratory: deploying containers on a gateway
  1. 3.1Signal preprocessing and filtering
  2. 3.2Local event and anomaly detection
  3. 3.3Lightweight models and TinyML
  4. 3.4Inference with ONNX Runtime and TensorFlow Lite
  5. 3.5Federated learning across devices
  6. 3.6Data compression for transmission
  7. 3.7Synchronisation with cloud models
  8. 3.8Model validation on constrained devices
  9. 3.9Energy consumption of computing
  10. 3.10Practice: anomaly detection on an edge device
  1. 4.1Local control of inverters and storage
  2. 4.2Microgrid management at the edge
  3. 4.3Distribution automation and self-healing
  4. 4.4Monitoring of transformer centres
  5. 4.5Electric vehicle charging management
  6. 4.6Local demand response
  7. 4.7Digital substations and protection virtualisation
  8. 4.8Edge in wind and solar farms
  9. 4.9Energy communities and local management
  10. 4.10Case study: edge controller for a microgrid
  1. 5.1Attack surface of distributed devices
  2. 5.2Secure boot and hardware root of trust
  3. 5.3Identity and certificate management
  4. 5.4Network segmentation and firewalls
  5. 5.5Compliance with IEC 62443-4-2
  6. 5.6Device health monitoring
  7. 5.7Reliability and redundancy
  8. 5.8Lifecycle and obsolescence
  9. 5.9Total cost of ownership models
  10. 5.10Case study: hardening a substation gateway
  1. 6.1Selecting the use case
  2. 6.2Functional and latency requirements
  3. 6.3Edge-cloud architecture
  4. 6.4Hardware and software selection
  5. 6.5Developing the edge application
  6. 6.6Security plan
  7. 6.7Testing and validation
  8. 6.8Large-scale deployment plan
  9. 6.9Report and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM Institute campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the course with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Docker, K3s, Node-RED, Azure IoT Edge, Eclipse Mosquitto, in free, academic or evaluation versions. Practical work is based on real procedures: deploying a containerised application on a substation gateway, anomaly detection with local inference, and designing an edge controller for a microgrid. Assessment is continuous by module, plus the final integrating project.

Accreditable laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented final project:safety case, test plan, compliance dossier and operating limits.
Industry-mentored:teaching staff with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, OT cyber security, DIH and compliance as its pillars.
Capstone project

Build it end to end

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

Selecting the use case

Understand edge computing architectures applied to energy.

Functional and latency requirements

Select hardware and software platforms for edge devices.

Edge-cloud architecture

Deploy containerised applications and data flows at the edge.

Hardware and software selection

Implement analytics and lightweight model inference on constrained devices.

Developing the edge application

Apply edge computing to microgrid, charging and substation control.

Career opportunities

Where this takes you

Industrial IoT engineerEdge systems architectNetwork automation engineerEmbedded software developerDigital substation specialistEnergy digital transformation consultant
Admissions & scholarships

Requirements

  • No prior degree required.
  • Internet connection and a computer capable of running the programme's software.
  • Spanish language proficiency sufficient to follow technical standards documentation.

Ready to start?

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

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FAQ

Frequently asked questions

The programme comprises 300 hours in total, spread over 3 to 5 months. That is an average of 10 to 15 hours per week, compatible with full-time work.

The course uses Docker, K3s, Node-RED, Azure IoT Edge and Eclipse Mosquitto. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

An in-house diploma from SEIUM Institute on passing continuous assessment and the final integrating project. It is in-house training, not an official Ministry qualification.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the advanced programme. Check the recognition policy when you enrol.

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