Q0061
AI can improve grids by forecasting demand and renewable output, detecting faults and equipment deterioration, estimating network conditions, prioritising maintenance and recommending operating actions. It is most useful as a decision-support and automation layer built on accurate sensors, network models and secure control systems. It cannot compensate for missing grid capacity, poor data or unclear operating accountability.
Malaysia's grid is becoming harder to operate as solar, batteries, electric vehicles and large loads grow. Better forecasts and faster analysis could increase the usable capacity and reliability of existing assets, but unsafe automation, opaque models or cyber weaknesses could create new system risks.
Machine-learning models can combine weather, calendar, customer and equipment data to improve forecasts of system demand, rooftop solar, utility-scale renewables and local network loading. Better forecasts help operators schedule generation, storage, reserves and maintenance.
Images, acoustic signals, temperature, dissolved-gas readings and operating histories can reveal abnormal transformers, lines, vegetation or switchgear. Predictive maintenance is valuable when it changes a real inspection or replacement decision, not merely when a model produces a risk score.
Distribution grids have fewer real-time measurements than transmission systems. AI can help estimate voltage and power flows between sensors, but results must remain consistent with electrical physics and expose uncertainty where the data are weak.
AI can rank switching, storage, demand response, voltage control and curtailment options under changing conditions. The system should recommend actions within approved limits, with deterministic protection and operator authority retained for safety-critical decisions.
Load growth, distributed-resource adoption and failure risk can be forecast at feeder or asset level. This can target conventional reinforcement and lower-cost alternatives more precisely.
Models need version control, testing, monitoring for drift, explainable outputs, audit logs and defined human accountability. The IEA identifies potential applications across renewable integration, operations, maintenance and network capacity, while stressing that deployment depends on digital foundations.
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