New Industrial PhD collaboration launched to bridge the AI “innovation gap” in the energy sector
AI seems to be everything everywhere all at once, but how can, or should, it be applied in the energy sector? A new project led by industrial PhD candidate Angelina Rubow in collaboration with E1 Management Consulting will dig through the data to find out.
As AI adoption accelerates in the energy sector - driven by digitalization and the energy transition - many solutions still struggle to move from promising research to real-world deployment. Utilities and grid operators often face an “innovation gap”, with barriers such as limited expertise, unclear data requirements and availability, uncertain economic benefits, and regulatory and technical constraints.
The project, “Bridging the Gap Between Theoretical AI and Their Practical Applications in the Energy Industry: Challenges, Opportunities, and Economic Implications”, aims to analyze existing AI approaches, identify implementation barriers, develop practical models, and assess the economic implications of AI adoption. Use cases with industry stakeholders may span asset management and predictive maintenance, operational decision support, and data-driven planning.
Leading the project is Angelina Rubow, the latest industrial PhD candidate at the Division of Applied Thermodynamics and Refrigeration, working in collaboration with E1 Management Consulting GmbH based in Hamburg, Germany. Angelina brings several years of experience as an energy industry consultant and an academic background in Sustainable Energy Engineering, with work spanning energy systems, energy efficiency, and machine learning. She is supervised by Prof. Hatef Madani and Dr. Nelson Sommerfeldt and looks forward to working with our colleagues at ETT and EGI.
By combining E1’s industry expertise with KTH’s academic resources, the collaboration aims to deliver actionable insights and practical pathways for responsible, value-creating AI in the energy sector.
For a peek into the project’s inspiration, Angelina’s 2025 master’s thesis finds an innovation gap related to data availability and can be downloaded here .
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