Artificial intelligence is quietly powering a renewable energy revolution. A recent review from the State Grid Information and Telecommunication Group of Beijing [39.9°N ,116.4°E] sheds light on just how deeply AI is embedded in our global transition to cleaner energy, and why this marriage of machine learning and sustainability could be the key to a more efficient, secure, and resilient energy future.
AI is already making wind farms, solar panels, and battery systems smarter and more reliable — but we also need to tackle the cybersecurity risks that come with it.
Predicting the Wind, Optimising the Sun
From wind turbines to rooftop solar panels, one of the biggest challenges in renewable energy is unpredictability. When will the wind blow? How much sunshine will we get tomorrow? AI, particularly deep learning models like LSTM (Long Short-Term Memory networks), is proving far better than traditional methods at forecasting these shifts. That means power grids can be managed more effectively, energy storage can be used more intelligently, and we can rely less on fossil fuels for backup.
In the case of wind energy, neural networks can now model the complex interplay of weather patterns, turbine conditions, and output data to fine-tune operations. The same goes for solar energy, where drones with AI-powered cameras are inspecting PV panels for faults, and algorithms are predicting energy output with greater precision than ever.
Keeping Batteries Healthy
AI is also enhancing the way we store energy. Smart algorithms monitor battery health, predict failures, and even learn the optimal times to charge and discharge to maximise lifespan and minimise cost. With demand for large-scale energy storage on the rise, these tools are essential to scaling renewable grids.
Security: The Flip Side of Smart
But intelligence brings risk. The review highlights growing concerns about data privacy and cyberattacks. AI systems require vast amounts of data to function — from weather patterns to grid usage to personal energy habits. If hacked or manipulated, the consequences could range from blackouts to equipment damage.
To combat this, researchers are developing secure architectures, federated learning (which trains models without exposing raw data), and explainable AI systems that make decisions more transparent. It’s a race to build both smarter and safer infrastructure.
Looking Ahead
Perhaps the most inspiring takeaway from this research is the sheer scope of AI’s impact. It isn’t just tweaking performance — it’s enabling entirely new ways of managing complex, decentralised energy systems. Whether it’s predicting energy demand during seasonal changes, coordinating thousands of microgrids, or detecting faults before they occur, AI is proving indispensable.
And the work isn’t limited to wind and solar. The paper explores AI’s use in hydropower, nuclear, hydrogen, geothermal and even biomass systems, showing how deeply it’s woven into our clean energy future.
So next time you hear about artificial intelligence, think beyond chatbots. Think wind farms that learn, batteries that adapt, and power systems that defend themselves. If done right, AI might not just support the energy transition — it might accelerate it.
Source
Artificial Intelligence in Renewable Energy Systems: Applications and Security Challenges, Energies 2025, 18, 1931, 2025-03-10
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