Despite growing public support for the UK’s net zero targets, recent reports indicate that the nation remains off-track to achieve net neutrality by 2030. Professor Piers Forster, Interim Chair of the Climate Change Committee, has urged the new Labour government, led by Keir Starmer, to act swiftly to correct the course. A key area for immediate attention is investment in new technologies, particularly in the renewable energy sector, where artificial intelligence (AI) is poised to play a crucial role.
Charlotte Enright, Head of Renewables at Northeast-based commercial finance operators Anglo Scottish Finance, has outlined how AI is transforming the renewables landscape. “AI is not just about smart gadgets; it’s reshaping how we manage energy,” she stated, highlighting its ability to predict energy demand peaks and troughs.
Enright explained that AI can analyse extensive power usage data to forecast periods of high demand, helping to balance the grid. For instance, it can identify when energy is most heavily consumed, whether from an afternoon tea break or during streaming services’ ad breaks. By predicting wind energy availability, AI can assist in understanding the potential output from turbines, aiding grid integration.
Karen Panetta, a fellow at the Institute of Electrical and Electronics Engineers, emphasised that AI enables better correlation of trends and forecasting. “This allows us to explore relationships and find ways to mitigate failures in the grid,” she said, enhancing efficiency in energy distribution.
AI is also revolutionising maintenance for renewable energy generators. Rather than waiting for faults to occur, companies are employing predictive maintenance powered by AI. Sensors placed on generators monitor data to forecast maintenance needs. “This is particularly beneficial for wind turbines located in remote areas,” Enright noted, allowing for strategic maintenance scheduling that minimises downtime.
Moreover, AI monitors temperature variations in solar panels, detecting hot spots that may indicate malfunctioning cells. In the interim, panels can be repositioned to optimise energy capture while maintenance is arranged.
The ability of AI to simulate and predict future weather conditions adds another layer of benefit to renewable energy management. “While renewable energy sources are ever-present, their availability fluctuates,” Enright stated. “AI-driven weather simulators provide insights into future energy capture potential, considering factors like urban airflow that can influence energy generation.”
As the renewable energy sector evolves, sustainability remains a crucial focus. Many renewable generators rely on rare earth metals, and their production can be energy-intensive. “AI accelerates the testing of new materials, allowing us to streamline trials,” Enright explained. “Additionally, AI can facilitate the recycling of generators once they reach the end of their lifespan, which is vital for sustainability.”
The transformative potential of AI in the renewable energy sector is becoming increasingly evident. From maximising generator uptime to predicting energy demand and adapting to ever-changing weather conditions, this technology is proving indispensable in the fight against climate change. As the UK strives to meet its ambitious net zero targets, harnessing AI may emerge as one of the most significant tools in achieving these goals.
