Energy Data Analytics and Machine Learning Training Course

Participants can map energy data sources, identifiers, time conventions and data flows for analysis.

• SCADA tags, historian records and equipment telemetry data structures
• Smart-meter interval data, meter events and customer account records
• Weather observations, numerical forecasts and renewable resource measurements
• Work orders, failure codes and asset hierarchy reference data
• Market prices, bids, dispatch schedules and imbalance settlement records
• Data lineage from source system through curated analytical dataset

Participants can extract, reshape and combine energy datasets using practical Python and SQL patterns.

• Python notebooks, pandas DataFrames and reproducible project folder structures
• SQL SELECT, JOIN, GROUP BY and window functions for intervals
• Reading CSV, Parquet and API outputs into analytical tables
• Reshaping wide SCADA tags into timestamped long-format observations
• Joining meter intervals with weather stations and asset identifiers
• Documenting dataset grain, refresh frequency and source-system ownership

Participants can diagnose and treat common data-quality issues in energy operational datasets.

• Missing interval detection using expected timestamp sequence checks
• Duplicate readings, late arrivals and out-of-order telemetry records
• Timezone conversion, daylight-saving changes and settlement-period alignment
• Range checks using engineering limits and plausible operating envelopes
• Outlier triage separating sensor faults from genuine operating events
• Imputation choices documented against downstream model and decision risks

Participants can identify demand, generation, asset and market patterns using exploratory analysis.

• Load-duration curves reveal demand distribution and peak-period exposure
• Seasonal decomposition separates trend, seasonal and residual components
• Correlation matrices screen weather, operational and market relationships
• Plotting turbine power curves against wind-speed measurements
• Segmenting consumption by customer class, tariff and geographic area
• Control charts highlight process shifts and unusual equipment behaviour

Participants can create and document features that represent energy-system behaviour without data leakage.

• Lag features capture prior load, output and sensor states
• Rolling windows calculate moving averages, volatility and operating ranges
• Calendar variables encode holidays, weekdays and settlement periods
• Weather-derived features include heating degree days and irradiance changes
• Asset-age, maintenance-history and utilisation features support failure modelling
• Feature leakage checks protect realistic model evaluation and deployment

Participants can build baseline time-series forecasts and select suitable forecast horizons.

• Forecast horizons matched to dispatch, trading and maintenance decisions
• Naive, seasonal-naive and moving-average baselines establish benchmark performance
• Autocorrelation and partial autocorrelation diagnose temporal dependence patterns
• Train-validation-test splits preserve chronological ordering in time-series datasets
• Rolling-origin evaluation tests performance across multiple forecast periods
• Point forecasts, prediction intervals and decision-specific uncertainty communication

Participants can compare statistical and machine learning models for key energy forecasting tasks.

• SARIMAX models incorporate seasonal patterns and external weather variables
• Gradient boosting forecasts nonlinear load and renewable-output relationships
• Random forest regression provides interpretable nonlinear benchmark models
• Quantile regression estimates forecast ranges for risk-sensitive decisions
• MAE, RMSE, MAPE and WAPE compared for energy forecasts
• Forecast reconciliation across feeder, region and system aggregation levels

Participants can estimate continuous operational outcomes and interpret model drivers responsibly.

• Linear regression establishes transparent baseline relationships between operating variables
• Regularisation with Ridge and Lasso controls unstable correlated predictors
• Gradient boosting estimates nonlinear heat-rate and efficiency relationships
• Residual analysis identifies systematic errors and unmodelled operating conditions
• Permutation importance ranks drivers without relying on model coefficients
• Partial dependence plots explain key variables within observed ranges

Participants can build classification models for failure risk, event prediction and operational alerts.

• Binary classification predicts failure events, alarms and quality exceptions
• Class imbalance addressed through weighting, resampling and threshold selection
• Confusion matrices connect model outcomes with maintenance decision consequences
• Precision, recall, F1 and PR-AUC assess rare-event performance
• Logistic regression supplies transparent risk-score baseline models
• Gradient-boosted classifiers capture nonlinear failure-risk patterns

Participants can identify anomalous readings, operating states and consumption patterns using suitable methods.

• Rule-based thresholds apply engineering limits and operating-state conditions
• Z-scores and median absolute deviation flag unusual sensor readings
• Isolation Forest detects multivariate anomalies within equipment telemetry
• Change-point detection identifies persistent shifts in process behaviour
• Residual-based alerts compare actual output against expected model output
• Alert prioritisation combines anomaly severity, asset criticality and persistence

Participants can design predictive-maintenance analyses that link condition data to maintenance decisions.

• Failure modes, effects and criticality analysis structures maintenance targets
• Asset hierarchy links components, systems, sites and fleet populations
• Survival analysis estimates time-to-failure and remaining useful life
• Condition indicators combine vibration, temperature, pressure and alarm histories
• Maintenance labels distinguish corrective, preventive and inspection work orders
• Risk matrices prioritise interventions by probability, consequence and detectability

Participants can incorporate location-based data into network, renewable and asset analyses.

• Coordinate reference systems prevent inaccurate distance and area calculations
• Spatial joins connect assets with weather, land-use and customer data
• Buffer analysis identifies assets exposed to local hazard zones
• Network topology represents feeders, substations, pipelines and connected assets
• Choropleth maps reveal geographic patterns in losses and consumption
• Site suitability criteria combine resource, access, constraint and proximity data

Participants can validate energy machine learning models against technical, operational and financial criteria.

• Cross-validation methods selected according to temporal and asset dependencies
• Backtesting compares predicted decisions against historical operating outcomes
• Error segmentation exposes performance differences by season, site and regime
• Cost-sensitive evaluation assigns consequences to false alerts and missed events
• Sensitivity analysis tests model stability under changing input assumptions
• Model acceptance criteria define minimum performance and operational usability

Participants can explain model results, identify risks and document appropriate limits of use.

• SHAP values explain individual predictions and global feature influence
• Counterfactual analysis tests conditions required for changed model outcomes
• Bias checks compare errors across customer groups, sites and regions
• Human-in-the-loop controls govern high-consequence operational recommendations
• Model cards document purpose, data, metrics, limitations and owners
• Escalation protocols address unexpected predictions and deteriorating performance

Participants can specify governance controls for operational energy data and analytical models.

• Data ownership assigns stewardship for operational, customer and market records
• Role-based access controls protect sensitive operational and customer data
• Data retention schedules balance analytical value, cost and legal obligations
• Quality scorecards track completeness, timeliness, validity and consistency
• Metadata catalogues record definitions, units, lineage and approved uses
• Third-party data agreements define access, permitted use and accountability

Participants can define a controlled process for deploying, monitoring and maintaining energy models.

• Version control manages code, datasets, features and model artefacts
• Training pipelines separate development, validation and production model stages
• Model registries record approved versions, owners and deployment status
• Data drift monitoring detects changing distributions in incoming operational records
• Performance monitoring compares live predictions with later actual outcomes
• Retraining triggers define review points, evidence requirements and approvals

Participants can translate model outputs into workflows, alerts and accountable business actions.

• Decision workflows specify users, actions, response times and escalation routes
• Alert design balances urgency, clarity, explanation and operator workload
• Maintenance prioritisation queues combine risk score, backlog and resource constraints
• Forecast handoffs align model outputs with planning and trading cycles
• Benefits tracking links analytical outputs with avoided costs and performance gains
• Implementation roadmaps sequence data, process, technology and capability changes

Participants can review an end-to-end analytics design for feasibility, value, risk and adoption.

• Use-case canvases connect business sponsors, decisions, metrics and required data
• Architecture reviews test source availability, refresh needs and integration constraints
• Model selection rationale compares interpretability, accuracy and maintenance effort
• Operating-model design assigns responsibilities across engineering, data and operations
• Risk registers capture safety, cyber, data-quality and model-failure concerns
• Executive briefings communicate findings, limits, investment needs and next actions

Participants can build, review and present a complete energy analytics solution from problem definition through implementation plan.

• Select an energy decision problem with defined value and users
• Prepare documented datasets using quality checks, joins and engineered features
• Build and compare baseline and machine learning candidate models
• Validate results through backtesting, error segmentation and decision thresholds
• Produce model documentation covering limitations, governance controls and ownership
• Present findings, recommendations and implementation roadmap to stakeholder panel