Postgraduate Diploma in Non-Technical Loss and Fraud Analytics
Online 800-hour Postgraduate Diploma in Non-Technical Loss and Fraud Analytics. 8 modules covering Royal Decree 1955/2000 (distribution and supply activities), Law 24/2013 (electricity sector) and Python (scikit-learn). Assigned tutor and open enrolment.
Postgraduate Diploma in Non-Technical Loss and Fraud Analytics — studying from Canada
Canada's energy sector spans oil and gas in Alberta, hydroelectricity in Québec and Manitoba, nuclear generation in Ontario, and growing wind and solar capacity across several provinces. Transmission and distribution networks are operated by provincial utilities such as Hydro-Québec and Ontario Power Generation, alongside private operators. Grid modernisation, interconnection and energy efficiency are priority areas for engineers. Regulation is handled by provincial energy boards and the federal Canada Energy Regulator.
Fees for Canada: C$1,400 (Canadian dollar), with instalments and employer sponsorship available.
Relevant bodies in Canada
Typical roles
Questions from Canada
Who licenses energy and power network engineers in Canada?
Power and energy engineers in Canada usually obtain Professional Engineer licences from provincial associations, which regulate the use of the title and require accredited education and experience. SEIUM's qualifications are private, not official state degrees and not accredited by a national body, so they do not themselves grant licensing. Employers or relevant bodies decide recognition, so verify this first.
Do Canadian utilities and energy employers value this type of training?
Canadian utilities and energy firms, including those in Ontario, Alberta and Quebec, typically prioritise licensed engineers and verifiable operational experience. A SEIUM qualification may complement your profile, and a strong final project can help demonstrate skills, but its acceptance is for each employer to decide. Ask employers what they expect before enrolling.
Can I study alongside a job, and what about funding and language?
Yes, the programmes are fully online, so you can study from home without a student visa and plan sessions around work. Specialist courses are 300 hours, postgraduate diplomas 800 and master's 1,900. Instruction is in English, and each student has an assigned technical tutor. Fees can be paid in instalments, and employer sponsorship is possible.
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
Classify technical and non-technical losses in distribution.
Calculate the technical losses of the network.
Prepare and combine data sources for detection.
Train machine learning models to detect anomalies.
Plan efficient inspection campaigns.
Design fraud recovery and prevention measures.
Apply ethical principles and data protection.
Who it's for
- Non-technical loss analyst who wants to specialise in Non-Technical Loss and Fraud Analytics.
- Distribution data scientist who wants to specialise in Non-Technical Loss and Fraud Analytics.
- Inspection campaign manager who wants to specialise in Non-Technical Loss and Fraud Analytics.
- Distribution network engineer who wants to specialise in Non-Technical Loss and Fraud Analytics.
- Energy anti-fraud analyst who wants to specialise in Non-Technical Loss and Fraud Analytics.
- Energy recovery manager who wants to specialise in Non-Technical Loss and Fraud Analytics.
- Engineering or science graduates seeking to join electricity distribution companies.
What you'll work with
- Royal Decree 1955/2000 (distribution and supply activities)
- Law 24/2013 (electricity sector)
- Royal Decree 1110/2007 (metering points)
- IEC 61968-9 (metering interfaces)
- Regulation (EU) 2016/679 (GDPR)
- ISO 37301 (compliance management systems)
8 modules · 800 h
1.1Technical versus non-technical losses1.2Energy balance of the network1.3Scale of losses by region1.4Economic and regulatory impact1.5Regulatory incentives for reduction1.6Types of fraud: bypasses, tampering and illegal connections1.7Metering and management errors1.8Customers without contracts and unregistered consumption1.9Legal aspects of electricity theft1.10Case study: loss balance of an urban area
2.1Losses in conductors and transformers2.2Load flows in low voltage2.3Estimation using load profiles2.4No-load and load losses2.5Network models and GIS quality2.6Calculation tools2.7Balances per transformer substation2.8Uncertainty in the calculation2.9Separation of technical and non-technical losses2.10Laboratory: calculation of technical losses in a LV circuit
3.1Smart meter load curves3.2Meter events: cover opened and magnetic field3.3Balances with totalising meters3.4Commercial and billing data3.5Data from previous inspections3.6Geographic and socioeconomic data3.7Data quality and cleansing3.8Derived variables and feature engineering3.9Labelling and inspection bias3.10Practice: building a detection dataset
4.1Expert rules and thresholds4.2Unsupervised anomaly detection4.3Supervised classification with gradient boosting4.4Neural networks on load curves4.5Class imbalance and appropriate metrics4.6Validation and overfitting prevention4.7Model interpretability4.8Graph and neighbourhood models4.9Continuous model updating4.10Laboratory: fraud classifier with XGBoost
5.1Prioritising inspections by expected value5.2Campaign performance and hit rate5.3Logistical planning of inspection teams5.4Field inspection procedures5.5Collection of evidence and reports5.6Inspector safety5.7Feedback to the model5.8Effectiveness indicators5.9Management of repeat offences5.10Workshop: prioritised campaign plan
6.1Re-billing of energy taken fraudulently6.2Regulatory settlement procedure6.3Legal actions and complaints6.4Technical anti-fraud measures6.5Elevated meters and sealed boxes6.6Concentric cables and twisted networks6.7Social programmes and regularisation6.8Communication with the public6.9Coordination with authorities6.10Case study: regularisation programme in vulnerable neighbourhoods
7.1Data protection in customer analytics7.2Bias and discrimination in models7.3Transparency towards the regulator7.4Governance of risk models7.5Algorithm auditing7.6Proportionality of inspections7.7Liability for false positives7.8High-risk AI regulation7.9Culture of integrity in the organisation7.10Practice: bias assessment of a detection model
8.1Diagnosis of losses in the area8.2Calculation of technical losses8.3Data preparation8.4Development of the detection model8.5Design of the inspection campaign8.6Prevention measures8.7Economic analysis of the programme8.8Monitoring indicators8.9Report and presentation8.10Final review with the tutor
How you'll learn
Training 100 % online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), QGIS, SQL, Power BI, in free, academic or evaluation versions. Tests are studied using real procedures for energy balances per transformer substation, targeted inspection campaigns and validation of detection models. Assessment is continuous by module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Classify technical and non-technical losses in distribution.
Calculate the technical losses of the network.
Prepare and combine data sources for detection.
Train machine learning models to detect anomalies.
Plan efficient inspection campaigns.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulatory reading.
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 comprises 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.
You work with Python (scikit-learn), QGIS, SQL and Power BI. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM own diploma on passing the continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the advanced programme. Check the credit recognition policy when you enrol.
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