WEMO 2025 (complet) - Flipbook - Page 40
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O U T LO O K
• Digital Twins: AI and particularly generative AI can enhance
digital twins – virtual replicas of physical energy systems –
allowing more accurate, dynamic, and predictive models. It
can simulate complex scenarios and optimize system behavior
in real time.
√ Scenario Simulation: using digital twins it will be possible
to generate thousands of operational scenarios to
stress-test power plants, wind or solar farms. This will
help improve resilience and e昀케ciency.
√ Real-Time Optimization: AI will make it possible to
update digital twins with real-time sensor data,
enabling predictive maintenance and performance
improvements.
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https://www.veri昀椀edmarketreports.com/blog/top-7-trends-in-high-temperature-cables/
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√ Improved renewable generation forecasting by creating
precise models for wind or solar output based on
improved weather prediction, historical data, and
geographic factors.
• Maintenance (Predictive and Preventive): Gen AI can
predict equipment failures and optimize maintenance
schedules, minimizing downtime and extending asset
lifespans. Applications include:
√ Anomaly Detection: Analyze sensor data to identify
subtle signs of wear in transformers, turbines, or grid
infrastructure.
√ Decision support and advance operation by using
optimization and AI to better operate the grid for
current and next operations.
√ Maintenance
Scheduling:
Generate
optimized
maintenance plans based on usage patterns and
historical failure data.
• Capital project delivery: Gen AI can help optimize schedules
and clarify optimization and delivery trade-o昀昀s leading to
gains in e昀케ciency (time and money).
√ Repair Guidance: Provide technicians with AI-generated
repair instructions For example, Gen AI could predict a
gas turbine’s failure risk by analyzing vibration patterns
and scheduling maintenance before a costly breakdown.
• Improved Equipment Design: Gen AI can accelerate the
design of energy equipment (e.g., turbines, solar panels,
batteries) by generating optimized designs based on
performance criteria, material constraints, and environmental
factors. Applications include:
√ Rapid Prototyping: Create and test virtual prototypes,
reducing the time and cost of physical iterations.
√ Customization: Design region-speci昀椀c equipment
tailored to local climate or grid requirements.
For example, Gen AI could help design next-generation
solar panels with improved heat dissipation, increasing
e昀케ciency in high-temperature regions.
√ Critical mode operations: Design operation rules to
keep equipment running until the next maintenance
scheduled.
• Improved Equipment Operations: AI can optimize the realtime operation of energy equipment, by improving e昀케ciency
and reducing waste. Application includes:
√ Dynamic Tuning: Adjust operating parameters in real
time to maximize output under stressful operations.
√ Energy E昀케ciency: Optimize fuel consumption in fossil
fuel plants or battery discharge rates in storage systems.
WEMO 2025
technologies are driving progress, but one stands apart. AI
and particularly generative AI should signi昀椀cantly impact
technological advancements in the energy sector – including
digital twins, modelling, equipment design and maintenance,
operations, grid management (see above), and material
innovation303. Below is a detailed breakdown of its potential
contributions, based on current trends and capabilities:
• Modelling and simulation: Gen AI can create highly
detailed and adaptive models for energy systems, improving
forecasting and decision-making. Applications include:
• Improved Electrical Grid Operations (as discussed earlier)
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AI impact on technological progress: Multiple
