AI is transforming clean energy – but where will the talent come from?
Posted on: 14 July, 2026
By Dr Mahmoud Dhimish
Senior lecturer and programme leader of MSc Renewable Energy and AI
The UK energy sector is rapidly expanding its use of artificial intelligence, from forecasting renewable generation to optimising electricity networks. Yet as adoption accelerates, the success of these technologies will depend not only on innovation, but on developing a workforce with the skills, confidence and technical understanding to apply AI effectively across the energy system.
Published on 8 June 2026, the Interim AI Adoption Plan: Clean Energy was written by Lucy Yu, AI Champion for Clean Energy, and draws on more than 80 survey responses, four multi-stakeholder roundtables and over 40 expert interviews.
The report finds that AI is already being applied across the electricity system to improve forecasting, optimise the performance of energy assets and support system planning. It also highlights its growing use in smarter grid operation, predictive maintenance and infrastructure planning.
However, it also identifies a major barrier to progress: capability, skills and system readiness. One of the report’s most significant findings is the challenge around capability, skills and system readiness. AI already has an established role in clean energy, supporting everything from forecasting and monitoring to optimisation and system management. The greater challenge now is ensuring there are enough professionals who understand both renewable energy systems and the AI tools increasingly being used to operate them.
A new type of energy professional
The report highlights several areas where AI is already beginning to make an impact, including forecasting renewable generation, maintaining critical infrastructure, optimising electricity networks and helping operators manage increasingly complex power flows.
However, moving from successful research and pilot projects to real operational deployment remains difficult.
AI systems depend on reliable data, careful validation, engineering understanding and appropriate governance. The report also highlights the importance of trust, safety and assurance, particularly where AI is applied to critical infrastructure.
The sector increasingly needs professionals who can work confidently across two disciplines. Alongside an understanding of renewable energy technologies and power systems, there is growing demand for expertise in data analysis, machine learning, modelling and responsible AI. As digital technologies become more embedded within energy infrastructure, this blend of skills is becoming increasingly valuable, yet remains relatively uncommon.
Why study renewable energy and AI?
This emerging skills gap is one of the reasons the University of the Built Environment has developed its new MSc Renewable Energy and AI.
Launching in September 2026, the fully online programme brings renewable energy engineering and artificial intelligence together within a single degree.
Students will explore renewable power systems alongside data analysis, AI, machine learning, digital modelling and responsible innovation, with learning supported through virtual laboratories, applied projects and real-world energy challenges.
The master’s in Renewable Energy and AI was developed around the principle that the future of energy will increasingly depend on the relationship between physical infrastructure and intelligent digital systems. Rather than treating AI as an additional specialist topic, the programme integrates these areas from the outset, enabling students to develop a holistic understanding of how digital technologies and energy systems interact.
From research to real-world application
The programme is also informed by current research into how AI can be applied to real renewable energy challenges.
My own work uses machine learning, deep learning and intelligent imaging to support areas including renewable energy forecasting, solar photovoltaic fault detection, system performance and predictive maintenance.
These applications reflect many of the opportunities identified in the new report.
In solar energy, AI is already helping operators analyse performance data and imagery, identify faults, improve system understanding and support more effective maintenance decisions. As these technologies continue to mature, industry requires professionals who understand not only their capabilities, but also their limitations. Effective deployment depends on proper validation, robust governance and the ability to exercise sound engineering judgement when interpreting AI-driven insights.
Preparing for the next phase of the energy transition
The independent report concludes that AI adoption across the energy sector remains uneven, with many promising applications still struggling to move from experimentation to widespread deployment.
As AI adoption across the energy sector continues to develop, education will need to evolve alongside the technology.
The clean energy transition will depend not only on deploying more solar panels, wind turbines, batteries and network infrastructure, but also on how effectively those assets are monitored, integrated and operated. Developing professionals with expertise across both energy systems and intelligent digital technologies is therefore becoming an increasingly important part of preparing the workforce for the next phase of the transition.
That is the skills challenge the University of the Built Environment’s new MSc Renewable Energy and AI has been designed to address.
The University of the Built Environment’s MSc Renewable Energy and AI begins in September 2026. Delivered fully online and part-time, the programme is designed for professionals who want to develop expertise at the intersection of renewable energy, data and artificial intelligence. For more information, please contact Mahmoud on m.dhimish@ube.ac.uk.
