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CourseAutomotive & Motorsport Applications open

Smart Metering and AMI Data Analytics Course

Online 300-hour course in Smart Metering and AMI data analytics. 6 modules covering IEC 62056 (DLMS/COSEM data exchange), Directive 2014/32/EU (measuring instruments, MID) and Python (pandas, scikit-learn). 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

Describe the architecture and protocols of an AMI infrastructure.

02

Apply validation, estimation and editing processes to metering data.

03

Segment consumption profiles using clustering techniques.

04

Develop forecasting and anomaly detection models using meter data.

05

Identify client-transformer-phase connectivity from AMI data.

06

Integrate metering analytics into operations, planning and billing.

07

Build an analytical dashboard using real AMI data.

Profile

Who it's for

  • Metering data analyst wishing to specialise in Smart Metering and AMI data analytics.
  • AMI systems engineer wishing to specialise in Smart Metering and AMI data analytics.
  • Data scientist in utilities wishing to specialise in Smart Metering and AMI data analytics.
  • Fraud reduction specialist wishing to specialise in Smart Metering and AMI data analytics.
  • Remote management project manager wishing to specialise in Smart Metering and AMI data analytics.
  • Energy digitalisation consultant wishing to specialise in Smart Metering and AMI data analytics.
  • Engineering or science graduates seeking to enter electricity distribution.
Standards & tools

What you'll work with

  • IEC 62056 (DLMS/COSEM data exchange)
  • Directive 2014/32/EU (measuring instruments, MID)
  • Regulation (EU) 2016/679 (data protection)
  • EN 50470-3 (static meters for active energy)
  • IEC 62052-11 (general requirements for measuring equipment)
  • ITU-T G.9904 (PLC PRIME)
  • ITU-T G.9903 (G3-PLC)
  • Royal Decree 1110/2007 (regulation on metering points)
Syllabus

6 modules · 300 h

  1. 1.1Smart meters and architecture
  2. 1.2Data concentrators
  3. 1.3PLC PRIME and G3 communications
  4. 1.4RF and NB-IoT communications
  5. 1.5Head-End system
  6. 1.6Meter Data Management
  7. 1.7DLMS/COSEM object model
  8. 1.8Mass meter deployment
  9. 1.9Remote reading and remote management
  10. 1.10Case study: smart meter remote management in Spain
  1. 2.1Validation, estimation and editing (VEE)
  2. 2.2Hourly and quarter-hourly curves
  3. 2.3Imputation of missing data
  4. 2.4Synchronisation and timestamps
  5. 2.5Time-series databases
  6. 2.6Data governance
  7. 2.7Personal data protection
  8. 2.8Communication quality
  9. 2.9Reading indicators
  10. 2.10Workshop: cleaning load curves in Python
  1. 3.1Typical consumption profiles
  2. 3.2Clustering with k-means
  3. 3.3Load indicators by transformer
  4. 3.4Detection of demand peaks
  5. 3.5Seasonality analysis
  6. 3.6Client, transformer and phase association
  7. 3.7Balances by transformer substation
  8. 3.8Visualisation of metering data
  9. 3.9Dashboards
  10. 3.10Practical: residential customer segmentation
  1. 4.1Demand forecasting with machine learning
  2. 4.2Anomaly and fraud detection
  3. 4.3Non-intrusive load monitoring (NILM)
  4. 4.4Undeclared self-consumption
  5. 4.5Phase identification
  6. 4.6Low-voltage voltage estimation
  7. 4.7Predictive maintenance of meters
  8. 4.8Explainable models for analysts
  9. 4.9Model validation
  10. 4.10Laboratory: anomalies with isolation forest
  1. 5.1Hourly billing and tariffs
  2. 5.2Demand response
  3. 5.3Consumer information
  4. 5.4Integration with ADMS and OMS
  5. 5.5Supply quality monitoring
  6. 5.6Network planning with AMI data
  7. 5.7Energy community allocation
  8. 5.8Metering cybersecurity
  9. 5.9Smart metering business case
  10. 5.10Case study: low-voltage voltage alerts
  1. 6.1Defining the business problem
  2. 6.2Inventory of data sources
  3. 6.3Preparation of the dataset
  4. 6.4Exploratory data analysis
  5. 6.5Proposed analytical model
  6. 6.6Validation of results
  7. 6.7Final dashboard
  8. 6.8Deployment 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 campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, which makes it possible to combine the programme with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas, scikit-learn), Microsoft Power BI, InfluxDB, Apache Spark, in free, academic or evaluation versions. Exercises are based on real procedures for cleaning and validating load curves, detecting consumption anomalies and segmenting customers by profile. Assessment is continuous, module by module, plus a final integrative project.

Accredited laboratories(EN ISO/IEC 17025) with dynamometer test bench, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-driven final project:safety case, test plan, compliance dossier and operational limits.
Industry mentorship:lecturers with track records in training, , eVTOL/UAM and flight test.
Flexible delivery(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, OT cyber, DIH and compliance as core pillars.
Capstone project

Build it end to end

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

Defining the business problem

Describe the architecture and protocols of an AMI infrastructure.

Inventory of data sources

Apply validation, estimation and editing processes to metering data.

Preparation of the dataset

Segment consumption profiles using clustering techniques.

Exploratory data analysis

Develop forecasting and anomaly detection models using meter data.

Proposed analytical model

Identify client-transformer-phase connectivity from AMI data.

Career opportunities

Where this takes you

Metering data analystAMI systems engineerData scientist in utilitiesFraud reduction specialistRemote management project managerEnergy digitalisation consultant
Admissions & scholarships

Requirements

  • No prior degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient Spanish 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.

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FAQ

Frequently asked questions

The programme comprises 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.

You will work with Python (pandas, scikit-learn), Microsoft Power BI, InfluxDB and Apache Spark. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.

An SEIUM in-house diploma upon passing continuous assessment and the integrative project. It is SEIUM's own training, not an official qualification from the Ministry.

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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