With energy demand soaring and infrastructure aging, artificial intelligence (AI) is stepping in to reshape the sector. A recent IBM study found that 74% of energy and utility companies are turning to AI to tackle data challenges, streamline operations and drive sustainability.
One of AI’s biggest advantages is smarter forecasting. By analysing vast datasets—weather patterns, historical consumption and renewable energy outputs—AI predicts both energy demand and renewable generation days in advance. Google and DeepMind, for instance, developed an AI system that predicts wind power output 36 hours ahead, increasing its commercial value by 20%. With better predictions, providers can balance supply and demand, reduce energy waste and cut costs.
Another breakthrough is predictive maintenance. Traditional inspection schedules lead to unnecessary checks or, worse, unexpected breakdowns. AI changes the game by using real-time sensor data to detect faults before they happen. National Grid ESO is already leveraging AI to monitor substations and power lines, preventing failures and extending infrastructure lifespan. Studies show AI-driven predictive maintenance reduces breakdowns by 70% and slashes maintenance costs by 25%.
However, AI itself is energy-intensive. The International Energy Agency (IEA) warns that growing AI adoption could significantly increase electricity consumption. The challenge is to harness AI’s benefits—grid optimisation, forecasting and efficiency—without inflating energy demand.
By integrating AI-driven automation and predictive technology, the energy sector can modernise aging infrastructure, cut waste and accelerate the transition to renewables. The future of energy isn’t just about generating power—it’s about using it smarter.
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