New Year Offer End Date: 30th April 2024
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Program

Energy Transition Analytics: Evidence to Action

Machine Learning for Energy Systems

Skills you will gain:

About Program:

Energy Transition Analytics: Evidence to Action is a focused workshop that explores how data, analytics, and evidence-based approaches can support the shift toward clean and sustainable energy systems. Participants will gain insights into energy data interpretation, decarbonization analysis, renewable energy planning, and policy-informed decision-making. The workshop is designed to help researchers, academicians, and professionals turn energy evidence into practical action for a more sustainable future.

Aim:

The aim of the workshop “Energy Transition Analytics: Evidence to Action” is to equip participants with the knowledge and analytical tools needed to interpret energy data, evaluate decarbonization pathways, and support evidence-based decision-making for sustainable energy systems and policy development.

Program Objectives:

  • Equip participants with analytical tools and methodologies to evaluate energy transition scenarios.

  • Enable evidence-based decision-making for policy, planning, and investment in sustainable energy.

  • Demonstrate how data-driven insights can guide actionable strategies for decarbonization and clean energy adoption.

  • Foster understanding of the economic, environmental, and technological factors influencing energy transitions.

  • Provide hands-on experience with real-world datasets, modeling, and analytics techniques relevant to energy systems.

What you will learn?

📅 Day 1: The Landscape of Transition — Mapping Global Trends

  • Understanding the shift from fossil fuels to multi-molecule systems (Hydrogen, Solar, Wind) using bibliometric and temporal data
  • Analyzing the  “Demand Awakening” from AI Data Centers and integrated renewable portfolios
  • Leveraging MDPI World Energy Statistics and Global Transition Progress datasets to identify leaders in energy productivity
  • Key Skill: Data scraping and preprocessing of academic metadata to identify emerging technology clusters
  • Hands-On (Google Colab): Bibliometric Trend Tracker — parse MDPI Open Access metadata to visualize 10-year growth of “Green Hydrogen” vs. “Carbon Capture” research

📅 Day 2: Predictive Modeling — Forecasting Generation & Demand

  • Using Machine Learning to manage renewable energy price and generation volatility
  • Understanding why 2026 energy markets experience “negative price” events and mitigation strategies
  • Introduction to CNN-LSTM and Random Forest models for solar and wind forecasting
  • Feature engineering with Energy Uncertainty Indexes (EUIs) and meteorological factors
  • Hands-On (Google Colab): Solar Output Estimator — build a Random Forest Regressor to predict daily kWh production using air temperature and irradiance data

📅 Day 3: Evidence to Action — Policy & Grid Optimization

  • Translating analytics into actionable evidence for industry stakeholders and policymakers
  • Quantitative policy analysis: evaluating socio-economic impacts of European Green Deal or National Green Hydrogen Missions using MCDA
  • Grid resilience: digital twins and smart meter analytics for predictive maintenance of infrastructure under extreme weather
  • The Prosumer Framework: analyzing peer-to-peer energy trading and active consumer participation
  • Hands-On (Google Colab): Policy Impact Simulator — rank energy transition strategies (e.g., Coal-to-Gas vs. Coal-to-Nuclear) using MCDA based on CO2 reduction, cost, and job creation metrics

Mentor Profile

Fee Plan

INR 1999 /- OR USD 50

Get an e-Certificate of Participation!

2024Certfiacte

Intended For :

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  • Students interested in renewable energy, sustainability, and energy analytics

  • PhD scholars, researchers, and academicians in energy, environment, and climate studies

  • Industry professionals in renewables, utilities, grid systems, and decarbonization

  • Policy analysts and government professionals working on energy transition strategies

  • Data scientists and AI/ML practitioners applying analytics in the energy sector

  • Entrepreneurs and climate-tech innovators building energy-focused solutions

Career Supporting Skills

Program Outcomes

  • Understand key concepts and challenges in the global energy transition.

  • Analyze and interpret energy and climate-related datasets.

  • Apply analytical approaches to evaluate renewable energy and decarbonization pathways.

  • Understand the role of data-driven insights in energy policy and planning.

  • Translate analytical findings into evidence-based strategies for sustainable energy systems.