Smart Grid Data Analytics for Utilities Training in Pretoria, South Africa by EduCore Global Skills

Module 1: Foundations of Smart Grids and Utility Data

Core competencies for Foundations of Smart Grids and Utility Data.

• Definition, scope, and drivers of smart grid transformation
• Smart grid architecture: generation, transmission, distribution, and customer domains
• Key data sources: smart meters, SCADA, sensors, PMUs, and outage systems
• The utility analytics value chain: from raw data to operational decisions
• Regulatory and industry context (e.g., EPRA, IEC 61968/61970, IEEE standards)

Module 2: Data Acquisition, Integration, and Architecture

Core competencies for Data Acquisition, Integration, and Architecture.

• AMI, SCADA, and IoT data flows and communication protocols
• Meter Data Management Systems (MDMS) and head-end systems
• Integrating data from CIS, GIS, OMS, ADMS, and asset systems
• Data lakes, warehouses, and cloud vs. on-premise architectures
• Real-time streaming vs. batch processing for grid data

Module 3: Data Quality, Preparation, and Governance

Core competencies for Data Quality, Preparation, and Governance.

• Common data issues: missing reads, duplicates, outliers, and time-sync errors
• Validation, estimation, and editing (VEE) of meter data
• Data cleaning and transformation with Excel, SQL, and Python
• Data governance, ownership, and master data management
• Data privacy, consumer consent, and cybersecurity (Kenya Data Protection Act, GDPR)

Module 4: Descriptive Analytics and Visualization

Core competencies for Descriptive Analytics and Visualization.

• Load profiling and consumption pattern analysis
• Key utility KPIs: SAIDI, SAIFI, CAIDI, load factor, and losses
• Time-series exploration and seasonality analysis
• Dashboards and reports with Power BI, Tableau, and Grafana
• Geospatial analytics and mapping with GIS

Module 5: Load Forecasting and Demand Analytics

Core competencies for Load Forecasting and Demand Analytics.

• Short-, medium-, and long-term forecasting horizons
• Statistical methods: regression, ARIMA, and exponential smoothing
• Machine learning forecasting: random forest, gradient boosting, and neural networks
• Weather, calendar, and economic drivers in forecasting models
• Forecast accuracy metrics (MAPE, RMSE) and model validation

Module 6: Loss Analytics and Revenue Protection

Core competencies for Loss Analytics and Revenue Protection.

• Technical vs. non-technical loss analysis at feeder and transformer level
• Energy balance and reconciliation using smart meter data
• Anomaly detection for theft, tampering, and meter faults
• Customer segmentation and risk scoring for field inspection targeting
• Measuring recovered revenue and analytics program ROI

Module 7: Grid Operations, Reliability, and Asset Analytics

Core competencies for Grid Operations, Reliability, and Asset Analytics.

• Outage analytics, fault location, and restoration performance
• Voltage and power quality analysis
• Transformer loading and overload detection
• Predictive maintenance and asset health indices
• Condition monitoring with sensor and inspection data

Module 8: Distributed Energy Resources and Customer Analytics

Core competencies for Distributed Energy Resources and Customer Analytics.

• Integrating solar PV, storage, and EV charging: data implications
• Hosting capacity and net-metering analytics
• Demand response, time-of-use tariffs, and load shifting analysis
• Customer segmentation, behavior analytics, and engagement
• Energy efficiency measurement and verification

Module 9: Advanced Analytics, AI, and Automation

Core competencies for Advanced Analytics, AI, and Automation.

• Machine learning workflow for utilities: problem framing to deployment
• Clustering, classification, and anomaly detection techniques
• Deep learning and time-series models for grid applications
• MLOps, model monitoring, and managing model drift
• Explainable AI and building operational trust in models

Module 10: Strategy, Implementation, and Emerging Trends

Core competencies for Strategy, Implementation, and Emerging Trends.

• Building a utility analytics roadmap and business case
• Team structure, skills, and analytics center of excellence
• Change management and embedding analytics in operations
• Measuring benefits, KPIs, and continuous improvement
• Emerging trends: digital twins, edge analytics, virtual power plants, and generative AI in utilities

How is Smart Grid Data Analytics for Utilities Training delivered?

Smart Grid Data Analytics for Utilities Training is delivered through instructor-led classroom, live online, private in-house corporate training depending on the selected cohort or organizational arrangement.

What are the fees for Smart Grid Data Analytics for Utilities Training?

The published course investment is KES 80,000 and USD 3,200. Location-specific fees are shown where configured.

How long is Smart Grid Data Analytics for Utilities Training?

Smart Grid Data Analytics for Utilities Training runs for 5 Days unless a customized corporate schedule is agreed.

Do participants receive a certificate for Smart Grid Data Analytics for Utilities Training?

Participants who complete the course receive a Certificate of Completion from EduCore Global Skills.

Where can I take Smart Grid Data Analytics for Utilities Training?

Smart Grid Data Analytics for Utilities Training is available in Pretoria, South Africa. Current dates are shown only where real sessions have been configured.

Who should attend Smart Grid Data Analytics for Utilities Training?

Smart Grid Data Analytics for Utilities Training is designed for Data Analysts, Reporting Officers, M&E Officers, Finance Analysts, Operations Managers, Professionals and organizations that need practical capability in Energy, Utilities, and Infrastructure Operations.

What are the prerequisites for Smart Grid Data Analytics for Utilities Training?

No mandatory prerequisite is required unless stated in the course outline. Practical workplace experience in the subject area is helpful.