How AI is Reshaping the Energy Sector
AI is reshaping the global energy sector by improving forecasting, grid optimisation, asset reliability, and system resilience, enabling more efficient, flexible, and sustainable energy systems.
The energy sector is undergoing rapid transformation as decentralisation, electrification, and renewable growth reshape system complexity. Traditional operational models can no longer keep pace with the demands of fluctuating supply, distributed assets, and increasingly dynamic markets. AI has emerged as a foundational tool for improving efficiency, reliability, and resilience across the value chain.
Author: Dr Hugo Quest, R&D Engineer at CSEM, Neuchâtel, Switzerland
Artificial intelligence (AI) has rapidly progressed from an academic discipline to a foundational technology reshaping the global energy sector. As energy systems evolve under the pressures of digitalisation, decarbonisation, decentralisation, and deregulation, AI offers capabilities uniquely suited to managing this growing complexity 1. By analysing vast and heterogeneous datasets, identifying patterns, and supporting real-time decision-making at scale, AI is enabling improvements in efficiency, affordability, sustainability, and resilience across the entire energy value chain 1,2. . Recent assessments suggest that the broad deployment of commercially available AI applications could generate annual cost savings exceeding US$200 billion by 2030 3, alongside substantial energy efficiency gains and emissions reductions. This potential is strengthened by a long track record of AI research and deployment in the power sector 2
From expert systems to deep learning: How AI reached today’s energy systems
AI in the energy sector has a history stretching over four decades 2. Early applications in the 1980s-1990s centred on expert systems and rule-based approaches, with landmark reviews in 1989 and 1997 cataloguing their use in power system control, diagnostics, and planning 4. These systems laid the groundwork for the more sophisticated approaches that followed. The 2010s onwards saw an unprecedented acceleration 3. Breakthroughs in machine learning (ML), deep learning (DL), reinforcement learning (RL), and the sharp decline in computational costs allowed researchers and industry to unlock new capabilities 2. The surge in connected devices, such as smart meters, sensors, and distributed energy resources, brought the data volumes necessary to scale AI-driven insights. This growth is clearly visible in research trends (Fig. 1). Learning-based approaches now dominate the landscape, representing the majority of published AI research overall, and a similarly large share in power-system studies. Annual publications at the AI and power systems intersection exceeded 35’000 in 2025, underscoring the rapid expansion of activity across domains such as forecasting, optimisation, planning, and anomaly detection.
Key AI applications reshaping the energy sector
AI applications in energy can be broadly grouped into system optimisation, asset lifecycle management, and critical-infrastructure security and resilience 3. .
System optimisation, trading, and demand response
As variable renewable energy (VRE) penetration grows, AI is becoming essential for maintaining reliable, efficient system operation.
Forecasting and real-time operations: Machine learning models now deliver highly accurate forecasts of load, market prices, and renewable output 1,6. Enhanced situational awareness enables operators to mitigate congestion, detect anomalies, and rebalance the system at short timescales.
Reinforcement Learning for grid and market optimisation: RL excels in sequential decision-making under uncertainty, and is increasingly deployed for electricity trading and dispatch optimisation 6. Applications include bidding strategies for aggregated flexible loads, optimisation of microgrids and energy communities, and support tools for transmission-system operation 2 .
Demand response and flexibility: AI is reshaping demand-side management by learning complex consumption patterns and optimising real-time load shifting 4. Large-scale flexible resources, such as commercial buildings or data centres, can act as virtual power plants. AI-enabled dynamic peak shaving in hyperscale data centres alone could unlock up to 100 GW of flexible capacity globally 3 .
Asset lifecycle management
AI enhances asset reliability and reduces costs across planning, operations, maintenance, and innovation 3
Predictive maintenance and digital twins: AI-driven predictive maintenance has reached high maturity across generation, transmission, distribution, and microgrids 1. Digital twins – virtual models continuously updated with real-time data – support scenario testing, optimise maintenance schedules, and reduce unplanned outages 2 .
Battery optimisation and materials innovation: AI accelerates discovery of new battery materials by screening vast chemical design spaces, while in operational settings, it optimises charging cycles, predicts degradation, and detects defects, ultimately extending asset lifetimes and improving safety 1 .
De-risking construction and permitting: AI tools increasingly support major infrastructure projects through automated document review, geospatial analysis, and intelligent permitting. Early deployments report up to 50% reductions in review time and overall project-development costs 1,3 .
AI-related cyber risks and system resilience
With digitalisation comes heightened exposure to cyber risks, but also powerful AI-enabled defence mechanisms.
Cybersecurity: Utilities face rapidly rising volumes of sophisticated cyberattacks. AI enhances defence by analysing network traffic in real time, identifying anomalies, and automating incident response 1 .
AI as both enabler and threat: Emerging regulations such as the EU AI Act classify AI systems used in energy-sector safety components as “high risk”, requiring stringent oversight. At the same time, malicious actors can use AI to automate and scale cyberattacks, making defensive AI even more critical 6 .
Resilient physical infrastructure: AI-enabled satellite imaging, drones, and IoT sensors can detect physical damage to critical infrastructure hundreds of times faster than traditional inspection methods. High- fidelity, AI-enhanced weather forecasts also improve system resilience, reducing computation time from hours to minutes 1,3 .
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