
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:
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Equip participants with analytical tools and methodologies to evaluate energy transition scenarios.
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Enable evidence-based decision-making for policy, planning, and investment in sustainable energy.
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Demonstrate how data-driven insights can guide actionable strategies for decarbonization and clean energy adoption.
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Foster understanding of the economic, environmental, and technological factors influencing energy transitions.
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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
Get an e-Certificate of Participation!

Intended For :
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Students interested in renewable energy, sustainability, and energy analytics
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PhD scholars, researchers, and academicians in energy, environment, and climate studies
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Industry professionals in renewables, utilities, grid systems, and decarbonization
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Policy analysts and government professionals working on energy transition strategies
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Data scientists and AI/ML practitioners applying analytics in the energy sector
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Entrepreneurs and climate-tech innovators building energy-focused solutions
Career Supporting Skills
Program Outcomes
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Understand key concepts and challenges in the global energy transition.
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Analyze and interpret energy and climate-related datasets.
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Apply analytical approaches to evaluate renewable energy and decarbonization pathways.
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Understand the role of data-driven insights in energy policy and planning.
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Translate analytical findings into evidence-based strategies for sustainable energy systems.
