Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
That explores how data, analytics, and evidence-based approaches can support the shift toward clean and sustainable energy systems. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Energy Transition Analytics: Evidence to Action is a focused course that explores how data, analytics, and evidence-based approaches can support the shift toward clean and sustainable energy systems.
📋 Course Objectives
1. Apply Artificial Intelligence methods to authentic research and industry problems.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in Artificial Intelligence
• R&D engineers and working professionals applying Artificial Intelligence in industry
• Academics and educators building research or teaching capacity in Artificial Intelligence
🚀 Key Learning Outcomes
• A demonstrable Artificial Intelligence project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
The Energy Data Landscape
• Generation, load, capacity and emissions data and where each is published
• Time resolution, missing intervals and timezone errors that corrupt analysis
• Capacity against energy, and the capacity factor that connects them
Characterising a System
• Load profiles, duck curves and the duration curve as an analytical device
• Variability, correlation between wind and solar output, and resource complementarity
• Marginal emissions factors against average, and why the distinction matters
Costing the Transition
• LCOE and its assumptions, plus the system costs it deliberately omits
• Value-adjusted metrics and the declining value of variable generation at high shares
• Storage economics, arbitrage and the cost of firming
Scenarios and Dispatch
• Capacity expansion and dispatch modelling in outline, with PyPSA as a tool
• Scenario design and the sensitivity to assumptions rather than the point estimate
• Grid constraints and curtailment as the binding limits on paper plans
Evidence That Changes Decisions
• Indicator selection for a policy or corporate audience
• Visualisation of uncertainty, and forecasts presented without false precision
• India-specific context: renewable targets, open access and grid integration
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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