AI in Energy Transition & Emissions Monitoring

Overview

Learn how AI/ML supports energy transition outcomes: forecasting demand & renewables, detecting anomalies and leaks, and monitoring emissions with sensor and satellite data. Convert raw data into decisions that improve reliability, safety, and ESG reporting.

Curriculum

• Energy-transition data landscape: sensors (SCADA/IIoT), satellites (e.g., multispectral), and weather feeds.
• Emissions basics: scopes, methane-specific challenges, and monitoring frameworks.
• Data engineering: ingest, clean, and align time-series & geospatial datasets (ETL/ELT patterns).
• Model patterns: forecasting, anomaly/leak detection, simple computer vision for flares/smoke.
• Geospatial concepts: rasters vs vectors, reprojection, and site-level overlays.
• Dashboards & alerting: KPI design, thresholds, and operator-friendly visuals.
• Governance & MLOps: model monitoring, drift, explainability, and audit trails.
• Case study: end-to-end pipeline from data to decision & report.

Benefits of Attending

• Hands-on patterns to stand up emissions & reliability monitoring quickly.
• Reusable checklists and templates for data pipelines, KPIs, and reporting.
• Confidence to brief stakeholders and integrate AI outputs into operational decisions.
• Clarity on where AI adds value vs. where rules and dashboards are sufficient.

What You Will Learn

By the end of this program, you will be able to:

• Explain how AI/ML supports emissions monitoring and energy transition objectives.
• Design a lightweight data pipeline for sensor/satellite inputs and basic ML tasks.
• Build simple forecasting and anomaly detection workflows and interpret outputs.
• Create stakeholder-ready dashboards and summaries for ESG & reliability use-cases.

Practitioner Profile