Power and Energy Machine Learning Data Course

Name: Power and Energy Machine Learning Data Management Course
Availability: InStock

Turn power and energy operational data into reviewed forecasts, anomaly evidence, maintenance signals, and accountable decisions.

Power and Energy Machine Learning Data Course

At a glance

Duration: 5 days
Format: Classroom
Cities: Casablanca, Athens, Manama, Tashkent, Barcelona, Jakarta and more
Next session: 12 – 16 October 2026, Casablanca
Average fee: 7,550 €

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Power and Energy Machine Learning Data Management Course

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Overview

Power and Energy Machine Learning Data Management Course is a five-day intermediate course for power-generation, grid, utility, renewable-energy, asset, reliability, data, and operations professionals, who leave with a Power and Energy ML Data-to-Decision Pack. Participants connect operational data, quality, time-series preparation, forecasting, anomaly detection, predictive maintenance, monitoring, and human decisions without building production models. The course supports evidence-based use of machine learning in energy operations. Agile Leaders Training Center provides training in power and energy machine learning data management.

Who Should Attend

Teams responsible for generation, grid, utility, or renewable-energy operations
Teams responsible for asset reliability and predictive maintenance decisions
Teams responsible for operational data, historians, meters, and sensors
Teams responsible for forecasting, planning, and performance analysis
Teams responsible for analytics governance, monitoring, and operational risk

The course assumes participants work with energy operations or data and leaves out model coding, control-system configuration, protection engineering, market trading, certification, and autonomous operational control.

Departments and Industries

The course supports machine-learning evaluation across power generation, transmission, distribution, renewable energy, utilities, industrial energy, and asset services.

Generation operations, performance, and maintenance
Transmission, distribution, and grid control support
Renewable forecasting and integration planning
Asset management, reliability, and condition monitoring
Data engineering, analytics, cybersecurity liaison, and governance

Learning Objectives

By the end of this course, participants will be able to:

Analyze energy data sources, context, quality, and ownership
Build time-series preparation and data-readiness profiles
Evaluate forecasting, anomaly, and predictive-maintenance evidence
Compare model uncertainty, errors, drift, and operational impacts
Apply monitoring, governance, and human decision controls
Build a Power and Energy ML Data-to-Decision Pack

Course Agenda

Day 1: Energy Data and ML Use Cases

Power and Energy ML Use Case Canvas
Generation, Grid, Meter, Weather, and Asset Data Map
Operational Technology and Information Technology Boundary
Forecast, Detect, Diagnose, Prioritize, and Optimize Task Matrix
Human Decision Authority and Accountability Canvas

Day 2: Data Quality and Time Series

Time-Series Timestamp, Interval, and Alignment Check
Missing Measurement and Bad-Data Treatment Guide
Data Provenance, Context, Units, and Lineage Profile
Seasonality, Weather, Load, and Event Feature Map
Energy ML Data-Readiness Scorecard

Day 3: Forecasting and Anomaly Evidence

Load and Renewable Forecast Evaluation Sheet
Baseline, Horizon, and Forecast Error Comparison
Anomaly and Fault Detection Signal Review
False Alarm and Missed Event Impact Matrix
State Estimation and Situational Awareness Questions

Day 4: Predictive Maintenance and Model Control

Asset Condition and Failure Mode Data Link
Predictive Maintenance Lead-Time Decision Map
Uncertainty, Generalization, and Operational Limitations
Model Drift and Performance Monitoring Plan
Cybersecurity, Change, Escalation, and Human Override Controls

Day 5: Energy ML Decision Practice

Suggested Exercise: Profile an Energy Time-Series Dataset
Suggested Exercise: Review a Forecast and Error Pattern
Suggested Exercise: Triage Anomaly and Maintenance Signals
Suggested Exercise: Define Monitoring and Escalation Controls
Capstone Exercise: Power and Energy ML Data-to-Decision Pack

Practical Exercises

The course uses suggested activities to connect energy data and model outputs to accountable operational decisions.

Suggested activity: frame an energy use case with the decision, data sources, operational context, owners, and exclusions
Suggested activity: profile time-series data for timestamps, gaps, units, seasonality, events, lineage, and readiness
Suggested activity: review forecast or anomaly evidence with baselines, errors, uncertainty, and operational consequences
Suggested activity: assemble a decision pack covering maintenance action, model monitoring, cybersecurity, change, override, and escalation

FAQs

Who suits power and energy machine learning data management training, and what does it assume?

The course suits generation, grid, utility, renewable, reliability, asset, data, and operations teams. It assumes familiarity with energy processes or operational data but not programming or machine-learning development.

How does energy machine learning data management differ from data science training?

Energy machine learning data management focuses on operational context, data readiness, forecast and anomaly evidence, maintenance decisions, monitoring, and governance. Data science training focuses on coding, algorithms, feature engineering, model tuning, and deployment.

What data supports machine learning in power and energy operations?

Relevant sources may include plant historians, supervisory systems, meters, sensors, weather, maintenance records, asset registries, alarms, outages, market or demand data, and operator logs, subject to quality, access, context, and security controls.

Why must energy ML models be monitored after deployment?

Operating conditions, assets, sensors, data pipelines, weather, demand, and maintenance practices change. Monitoring detects declining performance, drift, data failures, unreliable alerts, and conditions that require investigation, retraining, restriction, or human override.

Conclusion

Participants take back a Power and Energy ML Data-to-Decision Pack linking use cases, sources, time-series readiness, forecasting, anomaly evidence, maintenance signals, uncertainty, monitoring, cybersecurity, decision authority, and escalation. It improves collaboration between operational and data teams. It preserves accountable human control over energy decisions.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 1-20 of 58 events

Location	Dates	Duration	Mode	Price
Casablanca	Week 42, 2026 12 – 16 October 2026	5 Days	Onsite	€6,000
Athens	Week 42, 2026 12 – 16 October 2026	5 Days	Onsite	€7,500
Manama	Week 42, 2026 18 – 22 October 2026	5 Days	Onsite	€6,500
Tashkent	Week 44, 2026 1 – 5 November 2026	5 Days	Onsite	€8,000
Barcelona	Week 45, 2026 2 – 6 November 2026	5 Days	Onsite	€6,500
Zoom	Week 46, 2026 9 – 13 November 2026	5 Days	Online	€3,000
Jakarta	Week 46, 2026 9 – 13 November 2026	5 Days	Onsite	€8,000
Istanbul	Week 47, 2026 16 – 20 November 2026	5 Days	Onsite	€6,000
London	Week 48, 2026 23 – 27 November 2026	5 Days	Onsite	€6,500
Nice	Week 48, 2026 23 – 27 November 2026	5 Days	Onsite	€8,000
Geneva	Week 48, 2026 29 November – 3 December 2026	5 Days	Onsite	€8,000
Kuala Lumpur	Week 51, 2026 14 – 18 December 2026	5 Days	Onsite	€6,500
Abu Dhabi	Week 51, 2026 14 – 18 December 2026	5 Days	Onsite	€6,500
Cairo	Week 52, 2026 21 – 25 December 2026	5 Days	Onsite	€5,200
Montreux	Week 52, 2026 21 – 25 December 2026	5 Days	Onsite	€8,000
Porto	Week 53, 2026 28 December 2026 – 1 January 2027	5 Days	Onsite	€6,500
Prague	Week 01, 2027 4 – 8 January 2027	5 Days	Onsite	€7,500
Baku	Week 02, 2027 11 – 15 January 2027	5 Days	Onsite	€8,000
Bangkok	Week 02, 2027 17 – 21 January 2027	5 Days	Onsite	€8,000
Nairobi	Week 03, 2027 24 – 28 January 2027	5 Days	Onsite	€6,000

Frequently asked questions

What does this course cover?

OverviewPower and Energy Machine Learning Data Management Course is a five-day intermediate course for power-generation, grid, utility, renewable-energy, asset, reliability, data, and operations professionals, who leave with a Power and Energy ML Data-to-Decision Pack. Participants connect operational data, quality, time-series preparation, forecasting, a…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

Power and Energy Machine Learning Data Course